{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target Ensemble\n",
    "\n",
    "Apart from the main target, there are actually many auxilliary targets in the dataset. \n",
    "\n",
    "These targets are fundamentally related to the main target which make them potentially helpful to model. And because these targets have a wide range of correlations to the main targets, it means that we could potentially build some nice ensembles to boost our performance. \n",
    "\n",
    "In this notebook, we will \n",
    "1. Explore the auxilliary targets\n",
    "2. Select our favorite targets to include in the ensemble\n",
    "3. Create an ensemble of models trained on different targets\n",
    "4. Pickle and upload our ensemble model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Install dependencies\n",
    "!pip install -q numerapi pandas pyarrow matplotlib lightgbm scikit-learn cloudpickle seaborn scipy==1.10.1\n",
    "\n",
    "# Inline plots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Auxilliary Targets\n",
    "\n",
    "Let's start by taking a look at the different targets in the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-30 17:28:47,174 INFO numerapi.utils: target file already exists\n",
      "2023-08-30 17:28:47,176 INFO numerapi.utils: download complete\n",
      "2023-08-30 17:28:47,631 INFO numerapi.utils: target file already exists\n",
      "2023-08-30 17:28:47,632 INFO numerapi.utils: download complete\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>era</th>\n",
       "      <th>target</th>\n",
       "      <th>target_nomi_v4_20</th>\n",
       "      <th>target_nomi_v4_60</th>\n",
       "      <th>target_tyler_v4_20</th>\n",
       "      <th>target_tyler_v4_60</th>\n",
       "      <th>target_victor_v4_20</th>\n",
       "      <th>target_victor_v4_60</th>\n",
       "      <th>target_ralph_v4_20</th>\n",
       "      <th>target_ralph_v4_60</th>\n",
       "      <th>...</th>\n",
       "      <th>target_bravo_v4_20</th>\n",
       "      <th>target_bravo_v4_60</th>\n",
       "      <th>target_charlie_v4_20</th>\n",
       "      <th>target_charlie_v4_60</th>\n",
       "      <th>target_delta_v4_20</th>\n",
       "      <th>target_delta_v4_60</th>\n",
       "      <th>target_echo_v4_20</th>\n",
       "      <th>target_echo_v4_60</th>\n",
       "      <th>target_jeremy_v4_20</th>\n",
       "      <th>target_jeremy_v4_60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n003bba8a98662e4</th>\n",
       "      <td>0001</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n003bee128c2fcfc</th>\n",
       "      <td>0001</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0048ac83aff7194</th>\n",
       "      <td>0001</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00691bec80d3e02</th>\n",
       "      <td>0001</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00b8720a2fdc4f2</th>\n",
       "      <td>0001</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffc2d5e4b79a7ae</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffc7d24176548a4</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffc9844c1c7a6a9</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd79773f4109bb</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff87b21e4db902</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>606176 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   era   target  target_nomi_v4_20  target_nomi_v4_60  \\\n",
       "id                                                                      \n",
       "n003bba8a98662e4  0001 0.250000           0.250000           0.000000   \n",
       "n003bee128c2fcfc  0001 0.750000           0.750000           0.750000   \n",
       "n0048ac83aff7194  0001 0.250000           0.500000           0.250000   \n",
       "n00691bec80d3e02  0001 0.750000           0.750000           0.500000   \n",
       "n00b8720a2fdc4f2  0001 0.500000           0.750000           0.500000   \n",
       "...                ...      ...                ...                ...   \n",
       "nffc2d5e4b79a7ae  0573 0.250000           0.250000           0.500000   \n",
       "nffc7d24176548a4  0573 0.500000           0.500000           0.250000   \n",
       "nffc9844c1c7a6a9  0573 0.500000           0.500000           0.500000   \n",
       "nffd79773f4109bb  0573 0.500000           0.500000           0.500000   \n",
       "nfff87b21e4db902  0573 0.500000           0.750000           0.750000   \n",
       "\n",
       "                  target_tyler_v4_20  target_tyler_v4_60  target_victor_v4_20  \\\n",
       "id                                                                              \n",
       "n003bba8a98662e4            0.500000            0.250000             0.250000   \n",
       "n003bee128c2fcfc            0.750000            0.750000             0.750000   \n",
       "n0048ac83aff7194            0.500000            0.500000             0.500000   \n",
       "n00691bec80d3e02            0.500000            0.750000             0.750000   \n",
       "n00b8720a2fdc4f2            0.750000            0.750000             0.750000   \n",
       "...                              ...                 ...                  ...   \n",
       "nffc2d5e4b79a7ae            0.250000            0.500000             0.250000   \n",
       "nffc7d24176548a4            0.500000            0.250000             0.500000   \n",
       "nffc9844c1c7a6a9            0.500000            0.500000             0.500000   \n",
       "nffd79773f4109bb            0.750000            0.500000             0.500000   \n",
       "nfff87b21e4db902            1.000000            0.500000             0.500000   \n",
       "\n",
       "                  target_victor_v4_60  target_ralph_v4_20  target_ralph_v4_60  \\\n",
       "id                                                                              \n",
       "n003bba8a98662e4             0.000000            0.250000            0.250000   \n",
       "n003bee128c2fcfc             0.750000            0.750000            0.750000   \n",
       "n0048ac83aff7194             0.250000            0.500000            0.250000   \n",
       "n00691bec80d3e02             0.500000            0.750000            0.500000   \n",
       "n00b8720a2fdc4f2             0.500000            0.500000            0.500000   \n",
       "...                               ...                 ...                 ...   \n",
       "nffc2d5e4b79a7ae             0.500000            0.250000            0.500000   \n",
       "nffc7d24176548a4             0.500000            0.500000            0.250000   \n",
       "nffc9844c1c7a6a9             0.500000            0.250000            0.500000   \n",
       "nffd79773f4109bb             0.500000            0.500000            0.500000   \n",
       "nfff87b21e4db902             0.750000            0.500000            0.500000   \n",
       "\n",
       "                  ...  target_bravo_v4_20  target_bravo_v4_60  \\\n",
       "id                ...                                           \n",
       "n003bba8a98662e4  ...            0.250000            0.000000   \n",
       "n003bee128c2fcfc  ...            0.750000            1.000000   \n",
       "n0048ac83aff7194  ...            0.500000            0.250000   \n",
       "n00691bec80d3e02  ...            0.750000            0.500000   \n",
       "n00b8720a2fdc4f2  ...            0.750000            0.500000   \n",
       "...               ...                 ...                 ...   \n",
       "nffc2d5e4b79a7ae  ...            0.000000            0.250000   \n",
       "nffc7d24176548a4  ...            0.500000            0.500000   \n",
       "nffc9844c1c7a6a9  ...            0.500000            0.500000   \n",
       "nffd79773f4109bb  ...            0.750000            0.500000   \n",
       "nfff87b21e4db902  ...            0.500000            0.750000   \n",
       "\n",
       "                  target_charlie_v4_20  target_charlie_v4_60  \\\n",
       "id                                                             \n",
       "n003bba8a98662e4              0.500000              0.250000   \n",
       "n003bee128c2fcfc              0.750000              0.750000   \n",
       "n0048ac83aff7194              0.500000              0.250000   \n",
       "n00691bec80d3e02              0.750000              0.750000   \n",
       "n00b8720a2fdc4f2              0.750000              0.500000   \n",
       "...                                ...                   ...   \n",
       "nffc2d5e4b79a7ae              0.000000              0.250000   \n",
       "nffc7d24176548a4              0.500000              0.500000   \n",
       "nffc9844c1c7a6a9              0.500000              0.500000   \n",
       "nffd79773f4109bb              0.750000              0.750000   \n",
       "nfff87b21e4db902              0.500000              0.750000   \n",
       "\n",
       "                  target_delta_v4_20  target_delta_v4_60  target_echo_v4_20  \\\n",
       "id                                                                            \n",
       "n003bba8a98662e4            0.250000            0.000000           0.250000   \n",
       "n003bee128c2fcfc            0.750000            0.750000           0.750000   \n",
       "n0048ac83aff7194            0.500000            0.250000           0.500000   \n",
       "n00691bec80d3e02            0.500000            0.500000           0.750000   \n",
       "n00b8720a2fdc4f2            0.750000            0.500000           0.750000   \n",
       "...                              ...                 ...                ...   \n",
       "nffc2d5e4b79a7ae            0.250000            0.500000           0.000000   \n",
       "nffc7d24176548a4            0.500000            0.500000           0.500000   \n",
       "nffc9844c1c7a6a9            0.250000            0.500000           0.500000   \n",
       "nffd79773f4109bb            0.750000            0.500000           0.500000   \n",
       "nfff87b21e4db902            0.500000            0.500000           0.750000   \n",
       "\n",
       "                  target_echo_v4_60  target_jeremy_v4_20  target_jeremy_v4_60  \n",
       "id                                                                             \n",
       "n003bba8a98662e4           0.000000             0.250000             0.250000  \n",
       "n003bee128c2fcfc           0.750000             0.750000             1.000000  \n",
       "n0048ac83aff7194           0.250000             0.500000             0.250000  \n",
       "n00691bec80d3e02           0.500000             0.500000             0.500000  \n",
       "n00b8720a2fdc4f2           0.500000             0.500000             0.500000  \n",
       "...                             ...                  ...                  ...  \n",
       "nffc2d5e4b79a7ae           0.250000             0.000000             0.250000  \n",
       "nffc7d24176548a4           0.500000             0.500000             0.250000  \n",
       "nffc9844c1c7a6a9           0.500000             0.500000             0.500000  \n",
       "nffd79773f4109bb           0.500000             0.500000             0.500000  \n",
       "nfff87b21e4db902           0.750000             0.500000             0.500000  \n",
       "\n",
       "[606176 rows x 50 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from numerapi import NumerAPI\n",
    "\n",
    "# Download data\n",
    "napi = NumerAPI()\n",
    "napi.download_dataset(\"v4.2/train_int8.parquet\");\n",
    "napi.download_dataset(\"v4.2/features.json\");\n",
    "\n",
    "# Load data\n",
    "feature_metadata = json.load(open(\"v4.2/features.json\")) \n",
    "feature_cols = feature_metadata[\"feature_sets\"][\"medium\"]\n",
    "target_cols = feature_metadata[\"targets\"]\n",
    "train = pd.read_parquet(\"v4.2/train_int8.parquet\", columns=[\"era\"] + feature_cols + target_cols)\n",
    "\n",
    "# Downsample to every 4th era to reduce memory usage and speedup model training (suggested for Colab free tier)\n",
    "# Comment out the line below to use all the data (higher memory usage, slower model training, potentially better performance)\n",
    "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]\n",
    "\n",
    "# Print target columns\n",
    "train[[\"era\"] + target_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The main target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First thing to note is that `target` is just an alias for `target_cyrus_v4_20`, so we can drop this column for the rest of the notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop `target` column\n",
    "assert train[\"target\"].equals(train[\"target_cyrus_v4_20\"])\n",
    "target_names = target_cols[1:]\n",
    "targets_df = train[[\"era\"] + target_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target names\n",
    "\n",
    "At a high level, each target represents a different kind of stock market return\n",
    "- the `name` represents the type of stock market return (eg. residual to market/country/sector vs market/country/style)\n",
    "- the `_20` or `_60` suffix denotes the time horizon of the target (ie. 20 vs 60 market days)\n",
    "\n",
    "The reason why `target_cyrus_v4_20` is our main target is because it most closely matches the type of returns we want for our hedge fund. Just like how we are always in search for better features to include in the dataset, we are also always in search for better targets to make our main target. During our research, we often come up with targets we like but not as much as the main target, and these are instead released as auxilliary targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20</th>\n",
       "      <th>60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nomi</th>\n",
       "      <td>target_nomi_v4_20</td>\n",
       "      <td>target_nomi_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tyler</th>\n",
       "      <td>target_tyler_v4_20</td>\n",
       "      <td>target_tyler_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>victor</th>\n",
       "      <td>target_victor_v4_20</td>\n",
       "      <td>target_victor_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ralph</th>\n",
       "      <td>target_ralph_v4_20</td>\n",
       "      <td>target_ralph_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waldo</th>\n",
       "      <td>target_waldo_v4_20</td>\n",
       "      <td>target_waldo_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jerome</th>\n",
       "      <td>target_jerome_v4_20</td>\n",
       "      <td>target_jerome_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>janet</th>\n",
       "      <td>target_janet_v4_20</td>\n",
       "      <td>target_janet_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ben</th>\n",
       "      <td>target_ben_v4_20</td>\n",
       "      <td>target_ben_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alan</th>\n",
       "      <td>target_alan_v4_20</td>\n",
       "      <td>target_alan_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paul</th>\n",
       "      <td>target_paul_v4_20</td>\n",
       "      <td>target_paul_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>george</th>\n",
       "      <td>target_george_v4_20</td>\n",
       "      <td>target_george_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>william</th>\n",
       "      <td>target_william_v4_20</td>\n",
       "      <td>target_william_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arthur</th>\n",
       "      <td>target_arthur_v4_20</td>\n",
       "      <td>target_arthur_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thomas</th>\n",
       "      <td>target_thomas_v4_20</td>\n",
       "      <td>target_thomas_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyrus</th>\n",
       "      <td>target_cyrus_v4_20</td>\n",
       "      <td>target_cyrus_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caroline</th>\n",
       "      <td>target_caroline_v4_20</td>\n",
       "      <td>target_caroline_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sam</th>\n",
       "      <td>target_sam_v4_20</td>\n",
       "      <td>target_sam_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xerxes</th>\n",
       "      <td>target_xerxes_v4_20</td>\n",
       "      <td>target_xerxes_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>target_alpha_v4_20</td>\n",
       "      <td>target_alpha_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bravo</th>\n",
       "      <td>target_bravo_v4_20</td>\n",
       "      <td>target_bravo_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charlie</th>\n",
       "      <td>target_charlie_v4_20</td>\n",
       "      <td>target_charlie_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta</th>\n",
       "      <td>target_delta_v4_20</td>\n",
       "      <td>target_delta_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>echo</th>\n",
       "      <td>target_echo_v4_20</td>\n",
       "      <td>target_echo_v4_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jeremy</th>\n",
       "      <td>target_jeremy_v4_20</td>\n",
       "      <td>target_jeremy_v4_60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             20                     60\n",
       "name                                                  \n",
       "nomi          target_nomi_v4_20      target_nomi_v4_60\n",
       "tyler        target_tyler_v4_20     target_tyler_v4_60\n",
       "victor      target_victor_v4_20    target_victor_v4_60\n",
       "ralph        target_ralph_v4_20     target_ralph_v4_60\n",
       "waldo        target_waldo_v4_20     target_waldo_v4_60\n",
       "jerome      target_jerome_v4_20    target_jerome_v4_60\n",
       "janet        target_janet_v4_20     target_janet_v4_60\n",
       "ben            target_ben_v4_20       target_ben_v4_60\n",
       "alan          target_alan_v4_20      target_alan_v4_60\n",
       "paul          target_paul_v4_20      target_paul_v4_60\n",
       "george      target_george_v4_20    target_george_v4_60\n",
       "william    target_william_v4_20   target_william_v4_60\n",
       "arthur      target_arthur_v4_20    target_arthur_v4_60\n",
       "thomas      target_thomas_v4_20    target_thomas_v4_60\n",
       "cyrus        target_cyrus_v4_20     target_cyrus_v4_60\n",
       "caroline  target_caroline_v4_20  target_caroline_v4_60\n",
       "sam            target_sam_v4_20       target_sam_v4_60\n",
       "xerxes      target_xerxes_v4_20    target_xerxes_v4_60\n",
       "alpha        target_alpha_v4_20     target_alpha_v4_60\n",
       "bravo        target_bravo_v4_20     target_bravo_v4_60\n",
       "charlie    target_charlie_v4_20   target_charlie_v4_60\n",
       "delta        target_delta_v4_20     target_delta_v4_60\n",
       "echo          target_echo_v4_20      target_echo_v4_60\n",
       "jeremy      target_jeremy_v4_20    target_jeremy_v4_60"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print target names grouped by name and time horizon\n",
    "pd.set_option('display.max_rows', 100)\n",
    "t20s = [t for t in target_names if t.endswith(\"_20\")]\n",
    "t60s = [t for t in target_names if t.endswith(\"_60\")]\n",
    "names = [t[7:-6] for t in t20s]\n",
    "pd.DataFrame({\"name\": names,\"20\": t20s,\"60\": t60s}).set_index(\"name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target values\n",
    "\n",
    "Note that some targets are binned into 5 bins while others are binned into 7 bins.\n",
    "\n",
    "Unlike feature values which are integers ranging from 0-4, target values are floats which range from 0-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot target distributions\n",
    "targets_df[[\"target_cyrus_v4_20\", \"target_cyrus_v4_60\", \"target_william_v4_20\", \"target_william_v4_60\"]].plot(kind=\"hist\", bins=35, density=True, figsize=(8, 4), title=\"Target Distributions\", subplots=True, layout=(2, 2), ylabel=\"\", yticks=[]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also important to note that the auxilary targets can be `NaN`, but the primary target will never be `NaN`. Since we are using tree-based models here we won't need to do any special pre-processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqQAAAGJCAYAAABcnCHcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADyOElEQVR4nOydd5gTVRfG30lPdlO2LwssVXoVFRClSxE7FlQUe0MQsaKoiJ+CYFcQsYBiwYZiRVGahY4ovfftvaRn7vfHlCS7aQu7JBvO73kCszN3Zu5kJjPvnHsKxxhjIAiCIAiCIIgooYh2BwiCIAiCIIgzGxKkBEEQBEEQRFQhQUoQBEEQBEFEFRKkBEEQBEEQRFQhQUoQBEEQBEFEFRKkBEEQBEEQRFQhQUoQBEEQBEFEFRKkBEEQBEEQRFQhQUoQBEEQBEFEFRKkBEHEJKtWrQLHcfjqq6+i3ZWIyM/Px9VXX42UlBRwHIfXXnst2l0iCIJoNJAgJYgzmIULF4LjOOh0Opw4caLW8oEDB6JLly5R6Fnj48EHH8Qvv/yCKVOmYNGiRRgxYkTQthzHgeM4vPzyy7WWSedk06ZNde7DmXY+pe8x0Oeee+6JdvcIgqgDqmh3gCCI6ONwODBz5ky8+eab0e5Ko2XFihW4/PLL8fDDD0e8zuzZs3HvvffCYDDUa1/OpPN50UUX4eabb641v127dlHoDUEQJwsJUoIg0KNHD7z77ruYMmUKsrKyot2d00p1dTUSEhJOeTsFBQWwWCwRt+/Rowe2bt2KefPmYfLkyae8/5rbjofzabfbodFooFAEH8xr164dxo4dW+dtW63Wen8RIAji5KEhe4Ig8MQTT8Dj8WDmzJkh2x0+fBgcx2HhwoW1lnEch2nTpsl/T5s2DRzHYe/evRg7dizMZjPS0tLw1FNPgTGGY8eO4fLLL4fJZEJmZmbA4WsA8Hg8eOKJJ5CZmYmEhARcdtllOHbsWK1269evx4gRI2A2m2EwGDBgwAD89ddffm2kPu3cuRM33HADkpKScMEFF4Q85oMHD+Kaa65BcnIyDAYD+vTpgx9//FFeLg2TM8YwZ84cecg4HP369cPgwYMxa9Ys2Gy2kG3/++8/3HLLLWjdujV0Oh0yMzNx2223obi4OGD7SM8nACxfvhwXXHABLBYLEhMT0b59ezzxxBNh1+M4Dvfffz8++eQTtG/fHjqdDr169cKaNWtqtT1x4gRuu+02ZGRkQKvVonPnzvjggw/82kg+w4sXL8bUqVPRtGlTGAwGVFRUhO1LOCRXhc2bN6N///4wGAzyMS5duhSjRo1CVlYWtFot2rRpg+eeew4ej+eU90sQROSQhZQgCLRq1Qo333wz3n33XTz++OP1alW77rrr0LFjR8ycORM//vgj/ve//yE5ORnvvPMOBg8ejBdffBGffPIJHn74YZx77rno37+/3/rPP/88OI7DY489hoKCArz22msYOnQotm7dCr1eD0AYLh85ciR69eqFZ555BgqFAgsWLMDgwYPxxx9/4LzzzvPb5jXXXIOzzjoLL7zwAhhjQfuen5+P888/H1arFRMnTkRKSgo+/PBDXHbZZfjqq69w5ZVXon///li0aBFuuummoMPHwZg2bRr69++Pt99+O6SVdPny5Th48CBuvfVWZGZmYseOHZg/fz527NiBdevW1RLAkZ7PHTt24JJLLkG3bt0wffp0aLVa7N+/v5aQD8bq1avx+eefY+LEidBqtZg7dy5GjBiBDRs2yL6q+fn56NOnjyxg09LS8PPPP+P2229HRUUFJk2a5LfN5557DhqNBg8//DAcDgc0Gk3IPtjtdhQVFdWabzKZ/NYtLi7GyJEjMWbMGIwdOxYZGRkAhBeKxMRETJ48GYmJiVixYgWefvppVFRUYPbs2RF9DwRB1AOMIIgzlgULFjAAbOPGjezAgQNMpVKxiRMnyssHDBjAOnfuLP996NAhBoAtWLCg1rYAsGeeeUb++5lnnmEA2F133SXPc7vdrFmzZozjODZz5kx5fmlpKdPr9WzcuHHyvJUrVzIArGnTpqyiokKe/8UXXzAA7PXXX2eMMcbzPDvrrLPY8OHDGc/zcjur1cpatWrFLrroolp9uv766yP6fiZNmsQAsD/++EOeV1lZyVq1asVatmzJPB6P3/GPHz8+ou36th00aBDLzMxkVquVMeZ/TnyPpSafffYZA8DWrFkjz6vr+Xz11VcZAFZYWBhRv2seAwC2adMmed6RI0eYTqdjV155pTzv9ttvZ02aNGFFRUV+648ZM4aZzWb52KTz3bp164DHG6oPgT6fffaZ33EDYPPmzau1jUD7uvvuu5nBYGB2uz2ifhAEcerQkD1BEACA1q1b46abbsL8+fORm5tbb9u944475GmlUolzzjkHjDHcfvvt8nyLxYL27dvj4MGDtda/+eabYTQa5b+vvvpqNGnSBD/99BMAYOvWrdi3bx9uuOEGFBcXo6ioCEVFRaiursaQIUOwZs0a8Dzvt81II7B/+uknnHfeeX7D+omJibjrrrtw+PBh7Ny5M7IvIQTTpk1DXl4e5s2bF7SNZAkGvBbBPn36AAC2bNkScJ1Izqfk87p06dJa31Ek9O3bF7169ZL/zs7OxuWXX45ffvkFHo8HjDF8/fXXuPTSS8EYk89NUVERhg8fjvLy8lr9HzdunN/xhuPyyy/H8uXLa30GDRrk106r1eLWW2+ttb7vviorK1FUVIQLL7wQVqsVu3fvjrgfBEGcGiRICYKQmTp1Ktxud0S+h5GSnZ3t97fZbIZOp0Nqamqt+aWlpbXWP+uss/z+5jgObdu2xeHDhwEA+/btAyAImbS0NL/Pe++9B4fDgfLycr9ttGrVKqK+HzlyBO3bt681v2PHjvLyU6V///4YNGhQSF/SkpISPPDAA8jIyIBer0daWpp8DDWPzZdw5/O6665Dv379cMcddyAjIwNjxozBF198EbE4rXluACHIyGq1orCwEIWFhSgrK8P8+fNrnRtJHBYUFPitH+m5kWjWrBmGDh1a6yMNyUs0bdo04PD/jh07cOWVV8JsNsNkMiEtLU0Okgr13RIEUb+QDylBEDKtW7fG2LFjMX/+fDz++OO1lgcL1gkVAKJUKiOaByCkP2cwJPE0e/Zs9OjRI2CbxMREv7/rYoE7HTzzzDMYOHAg3nnnnYCR+tdeey3+/vtvPPLII+jRowcSExPB8zxGjBgRUjyGO596vR5r1qzBypUr8eOPP2LZsmX4/PPPMXjwYPz6669Bz1OkSH0bO3Ysxo0bF7BNt27davWpIQi03bKyMgwYMAAmkwnTp09HmzZtoNPpsGXLFjz22GMnZTUmCOLkIEFKEIQfU6dOxccff4wXX3yx1rKkpCQAwoPcl/qwFAZDsoBKMMawf/9+Wci0adMGgBDEMnTo0Hrdd4sWLbBnz55a86Wh3BYtWtTLfgYMGICBAwfixRdfxNNPP+23rLS0FL///jueffZZv2U1v5dghDqfAKBQKDBkyBAMGTIEr7zyCl544QU8+eSTWLlyZdjvM1Af9u7dC4PBgLS0NACA0WiEx+Op93NTH6xatQrFxcVYsmSJXzDdoUOHotgrgjgzoSF7giD8aNOmDcaOHYt33nkHeXl5fstMJhNSU1NrpfaZO3dug/Xno48+QmVlpfz3V199hdzcXIwcORIA0KtXL7Rp0wYvvfQSqqqqaq1fWFh40vu++OKLsWHDBqxdu1aeV11djfnz56Nly5bo1KnTSW+7JpIv6fz58/3mS1bKmtbjSEuThjqfJSUltdpLVmaHwxF222vXrvXzAT127BiWLl2KYcOGQalUQqlUYvTo0fj666+xffv2WuufyrmpDwJ9t06ns0GvZ4IgAkMWUoIgavHkk09i0aJF2LNnDzp37uy37I477sDMmTNxxx134JxzzsGaNWuwd+/eButLcnIyLrjgAtx6663Iz8/Ha6+9hrZt2+LOO+8EIFj43nvvPYwcORKdO3fGrbfeiqZNm+LEiRNYuXIlTCYTvv/++5Pa9+OPP47PPvsMI0eOxMSJE5GcnIwPP/wQhw4dwtdffx0yYXtdGTBgAAYMGIDVq1f7zTeZTOjfvz9mzZoFl8uFpk2b4tdff62TFS/Y+Zw+fTrWrFmDUaNGoUWLFigoKMDcuXPRrFmzsPlZAaBLly4YPny4X9onAHj22WflNjNnzsTKlSvRu3dv3HnnnejUqRNKSkqwZcsW/PbbbwFFcV3Yu3cvPv7441rzMzIycNFFF4Vc9/zzz0dSUhLGjRuHiRMnguM4LFq06KRcRwiCODVIkBIEUYu2bdti7Nix+PDDD2ste/rpp1FYWIivvvoKX3zxBUaOHImff/4Z6enpDdKXJ554Av/99x9mzJiByspKDBkyBHPnzvWrsjNw4ECsXbsWzz33HN566y1UVVUhMzMTvXv3xt13333S+87IyMDff/+Nxx57DG+++Sbsdju6deuG77//HqNGjaqPw/Nj2rRptaLDAeDTTz/FhAkTMGfOHDDGMGzYMPz8888R54sNdj4vu+wyHD58GB988AGKioqQmpqKAQMG4Nlnn4XZbA673QEDBqBv37549tlncfToUXTq1AkLFy708wvNyMjAhg0bMH36dCxZsgRz585FSkoKOnfuHNSNoC5IUfWB+hZOkKakpOCHH37AQw89hKlTpyIpKQljx47FkCFDMHz48FPuG0EQkcMxehUkCIIg6gjHcRg/fjzeeuutaHeFIIg4gHxICYIgCIIgiKhCgpQgCIIgCIKIKiRICYIgCIIgiKhCQU0EQRBEnaHwA4Ig6hOykBIEQRAEQRBRhQQpQRAEQRAEEVUa5ZA9z/PIycmB0WgMWlubIAiCIAiCiB6MMVRWViIrKytsIZFGKUhzcnLQvHnzaHeDIAiCIAiCCMOxY8fQrFmzkG0apSA1Go0AhAM0mUxR7g1BEARBEARRk4qKCjRv3lzWbaFolIJUGqY3mUwkSAmCIAiCIGKYSNwrKaiJIAiCIAiCiCokSAmCIAiCIIioQoKUIAiCIAiCiCokSAmCIAiCIIioQoKUIAiCIAiCiCokSAmCIAiCIIioQoKUIAiCIAiCiCokSAmCIAiCIIioQoKUIAiCIAiCiCokSOMcl9OJr2dMw65Vv0a7KwRBEARBEAFplKVDich599EHUWBJw7aVf0Hz/U9I4j0Ydcs4ZHc/J9pdIwiCIAiCAEAW0rinnBPfOTgOTqMF+eYUfPD1Unz05CPR7RhBEARBEIRInQTpjBkzcO6558JoNCI9PR1XXHEF9uzZ49fGbrdj/PjxSElJQWJiIkaPHo38/Hy/NkePHsWoUaNgMBiQnp6ORx55BG63+9SPhvDDabXCmZAIAMgsL4apuAAKpwNQKJFXaY1y7wiCIAiCIATqJEhXr16N8ePHY926dVi+fDlcLheGDRuG6upquc2DDz6I77//Hl9++SVWr16NnJwcXHXVVfJyj8eDUaNGwel04u+//8aHH36IhQsX4umnn66/oyIAAKsXvQemUgO8Bzc8/iQmvzkXCZXl0e4WQRAEQRCEH3XyIV22bJnf3wsXLkR6ejo2b96M/v37o7y8HO+//z4+/fRTDB48GACwYMECdOzYEevWrUOfPn3w66+/YufOnfjtt9+QkZGBHj164LnnnsNjjz2GadOmQaPR1N/RneHs2bkHSEqD2loNU0YmAICTl3LBViMIgiAIgjitnJIPaXm5YG1LTk4GAGzevBkulwtDhw6V23To0AHZ2dlYu3YtAGDt2rXo2rUrMjIy5DbDhw9HRUUFduzYEXA/DocDFRUVfh8iPFWccHp1Drs8T5KhjCNBShAEQRBEbHDSgpTneUyaNAn9+vVDly5dAAB5eXnQaDSwWCx+bTMyMpCXlye38RWj0nJpWSBmzJgBs9ksf5o3b36y3T6jcBpE/1GLUZ4nC9Io9IcgCIIgCCIQJy1Ix48fj+3bt2Px4sX12Z+ATJkyBeXl5fLn2LFjDb7Pxs76Lz8Gr9ECjMeQG8fJ82XDKFlICYIgCIKIEU5KkN5///344YcfsHLlSjRr1kyen5mZCafTibKyMr/2+fn5yMzMlNvUjLqX/pba1ESr1cJkMvl9iND88/d6AIDKZkVmh07yfOmEk4WUIAiCIIhYoU6ClDGG+++/H9988w1WrFiBVq1a+S3v1asX1Go1fv/9d3nenj17cPToUfTt2xcA0LdvX2zbtg0FBQVym+XLl8NkMqFTp04g6ocKjyA5tTb/9E6cZBklCylBEARBEDFCnaLsx48fj08//RRLly6F0WiUfT7NZjP0ej3MZjNuv/12TJ48GcnJyTCZTJgwYQL69u2LPn36AACGDRuGTp064aabbsKsWbOQl5eHqVOnYvz48dBqtfV/hGcoDkMCACDF4P+dSjqUgpoIgiAIgogV6iRI3377bQDAwIED/eYvWLAAt9xyCwDg1VdfhUKhwOjRo+FwODB8+HDMnTtXbqtUKvHDDz/g3nvvRd++fZGQkIBx48Zh+vTpp3YkhMyuVb/Co9MDAC647DK/ZV4L6enuFUEQBEEQRGDqJEgZC+95qNPpMGfOHMyZMydomxYtWuCnn36qy66JOvD3Tz8BBguUdiva9Rvkt0yhEJQoI0VKEARBEESMQLXs45ASmwsAoLXWLg+qIB9SgiAIgiBiDBKkcYhDbwAAmNS1RadCTJZPgpQgCIIgiFiBBGmccXz7P3CLgrRXv361ltOQPUEQBEEQsQYJ0jhj5eLPAI6DwmnHuVeNqbVcqRTdhslCShAEQRBEjECCNM4oq6wCAKhttoDLlUrRQkp6lCAIgiCIGIEEaZwhJ0IIkhFBoVQKE2QhJQiCIAgiRiBBGmdIOpQLUhxUpaIhe4IgCIIgYgsSpHGGJEO5ICljVaKFlIKaCIIgCIKIFUiQxhlMlqSBFalSTRZSgiAIgiBiCxKkcQYvWj6DWUiVajUAqmVPEARBEETsQII0bgmsSLVarTBBgpQgCIIgiBiBBGmcEc6HVKnRSC1OR3cIgiAIgiDCQoI0zpAFaRALqVojWEhpyJ4gCIIgiFiBBGmcEU5oamjIniAIgiCIGIMEaZwSTG7uPHxCbECClCAIgiCI2IAEadzB+fxbG3NJmtiMg8vpPD1dIgiCIAiCCAEJ0jhDqlEfTJAqOe8pd1VXNXyHCIIgCIIgwkCCNM5gYSykCijlaVtl+WnoEUEQBEEQRGhIkMYbom9oMBdRhc8pd1SRhZQgCIIgiOhDgjTOkIbsg51YPwtpFVlICYIgCIKIPiRI4w7JQhrYRKr0OeVOq/W09IggCIIgCCIUJEjjDBZmyF4JJTjRjOqw2U5XtwiCIAiCIIJCgjTekIbsucCnVgEFFGIjp7X6dPWKIAiCIAgiKCRI4ww5yj7ImVUwDpwkSO3209UtgiAIgiCIoNRZkK5ZswaXXnopsrKywHEcvv32W7/lHMcF/MyePVtu07Jly1rLZ86cecoHQ0Aeq1cEGbP3tZC6HJQYnyAIgiCI6FNnQVpdXY3u3btjzpw5AZfn5ub6fT744ANwHIfRo0f7tZs+fbpfuwkTJpzcERD+yII0yJA98wpSt4sEKUEQBEEQ0UdV1xVGjhyJkSNHBl2emZnp9/fSpUsxaNAgtG7d2m++0Wis1ZY4daSgJqUysCDl4DNk73Cctn4RBEEQBEEEo0F9SPPz8/Hjjz/i9ttvr7Vs5syZSElJQc+ePTF79my43e6g23E4HKioqPD7EMEQLaQKZcClCnCyhdTjcp22XhEEQRAEQQSjzhbSuvDhhx/CaDTiqquu8ps/ceJEnH322UhOTsbff/+NKVOmIDc3F6+88krA7cyYMQPPPvtsQ3Y1bpAS4yuVQXxIGQcF4wAO8LhJkBIEQRAEEX0aVJB+8MEHuPHGG6HT6fzmT548WZ7u1q0bNBoN7r77bsyYMQNarbbWdqZMmeK3TkVFBZo3b95wHW/MiL6jSmXgUyt4kIo+pG7PaesWQRAEQRBEMBpMkP7xxx/Ys2cPPv/887Bte/fuDbfbjcOHD6N9+/a1lmu12oBClaiNbCFVhR+yd7uCu0kQBEEQBEGcLhrMh/T9999Hr1690L1797Btt27dCoVCgfT09IbqzpmDGNSkUgYWpEJIk3DaeQ9ZSAmCIAiCiD51tpBWVVVh//798t+HDh3C1q1bkZycjOzsbADCkPqXX36Jl19+udb6a9euxfr16zFo0CAYjUasXbsWDz74IMaOHYukpKRTOBRCQBSkGk3ApX5BTTx/2npFEARBEAQRjDoL0k2bNmHQoEHy35Jv57hx47Bw4UIAwOLFi8EYw/XXX19rfa1Wi8WLF2PatGlwOBxo1aoVHnzwQT8fUeLkkdM+qdUBlyuYjyAlCylBEARBEDFAnQXpwIEDwRgL2eauu+7CXXfdFXDZ2WefjXXr1tV1t0SkiIJUowkiSH3ykPJkISUIgiAIIgagWvbxhmQhDTJkz/kM2fN86BcLgiAIgiCI0wEJ0jiDiWJTo9UFXK6AMGwPkIWUIAiCIIjYgARpvCEN2esCC1I/CykZSAmCIAiCiAFIkMYboiBVB7WQen1IGSMLKUEQBEEQ0YcEaRzhcjplQapLTAzYRsEJ1ZoAgA8TnEYQBEEQBHE6IEEaR9jLyuRpjcEQsI3vkD3JUYIgCIIgYgESpHGEvbpCntYaEgK2UQDykL2HRuwJgiAIgogBSJDGEY7qSnlab7IEbMMBPhZS7jT0iiAIgiAIIjQkSOMIe6XXQqo3mgO24eBN+wQSpARBEARBxAAkSOMIh9UmT6sTagc1OZ1OKDifKPvT1jOCIAiCIIjgkCCNI5w2qzDBGNQBKjVV2qoAeIfswdHpJwiCIAgi+pAiiSN8BWkgbNYagpSG7AmCIAiCiAFIkMYRTodDmAgiSKvsgmDlpNNOFlKCIAiCIGIAUiRxhMspCFIuiHeo3W4H4BNlz5GFlCAIgiCI6EOCNI5w2UJbSO0OIeiJoyF7giAIgiBiCBKkcYTL7RQmgghSh8PfQgqykBIEQRAEEQOQII0jPC4XAIALZiGVhuzlPKR0+gmCIAiCiD6kSOIIj8stTAQRpG6nIFjJQkoQBEEQRCxBgjSOcLkFQRosqMnlcorLSZASBEEQBBE7kCCNIzwejzARzEIqWlDJQkoQBEEQRCxBgjSO4OsoSBlF2RMEQRAEEQOQII0jJAspF6RIvSRYacieIAiCIIhYggRpHOHx8MJEEAsp7xYEqUKu1ESClCAIgiCI6FNnQbpmzRpceumlyMrKAsdx+Pbbb/2W33LLLeA4zu8zYsQIvzYlJSW48cYbYTKZYLFYcPvtt6OqquqUDoQAeF4QosGCmniXKEgZCVGCIAiCIGKHOgvS6upqdO/eHXPmzAnaZsSIEcjNzZU/n332md/yG2+8ETt27MDy5cvxww8/YM2aNbjrrrvq3nvCD56Fs5AKyzkqHUoQBEEQRAyhqusKI0eOxMiRI0O20Wq1yMzMDLhs165dWLZsGTZu3IhzzjkHAPDmm2/i4osvxksvvYSsrKy6dokQkSykwQQpxCF9irInCIIgCCKWaBAf0lWrViE9PR3t27fHvffei+LiYnnZ2rVrYbFYZDEKAEOHDoVCocD69esDbs/hcKCiosLvQ9SGZ6GH7CUDqjfKniAIgiAIIvrUuyAdMWIEPvroI/z+++948cUXsXr1aowcOVKOAM/Ly0N6errfOiqVCsnJycjLywu4zRkzZsBsNsuf5s2b13e34wImWUaDKE3OIwlWspASBEEQBBE71HnIPhxjxoyRp7t27Ypu3bqhTZs2WLVqFYYMGXJS25wyZQomT54s/11RUUGiNACSHg1Wy56JQ/qyhZT0KEEQBEEQMUCDp31q3bo1UlNTsX//fgBAZmYmCgoK/Nq43W6UlJQE9TvVarUwmUx+H6I2PAvtQ8p5BAUqW0gpMT5BEARBEDFAgwvS48ePo7i4GE2aNAEA9O3bF2VlZdi8ebPcZsWKFeB5Hr17927o7sQ1kgwNJjM52YdUOO1kISUIgiAIIhao85B9VVWVbO0EgEOHDmHr1q1ITk5GcnIynn32WYwePRqZmZk4cOAAHn30UbRt2xbDhw8HAHTs2BEjRozAnXfeiXnz5sHlcuH+++/HmDFjKML+FGEIE2UvCVJGQU0EQRAEQcQOdbaQbtq0CT179kTPnj0BAJMnT0bPnj3x9NNPQ6lU4r///sNll12Gdu3a4fbbb0evXr3wxx9/QKvVytv45JNP0KFDBwwZMgQXX3wxLrjgAsyfP7/+juoMRfYhDbKcE4WoN+1Tw/eJIAiCIAgiHHW2kA4cONAbzR2AX375Jew2kpOT8emnn9Z110QYmKgwgwU1cZT2iSAIgiCIGIRq2ccRLMCUL9JQPUdR9gRBEARBxBAkSOOIcBZPjhdON1lICYIgCIKIJUiQxhFylH2QIXuykBIEQRAEEYuQII0rOJ9/AyzlpaAmOu0EQRAEQcQOpEziCBYmeF4SopKllCcLKUEQBEEQMQAJ0rgijIW05pD96egSQRAEQRBEGEiQxhFeC2kQH9IaQU3gAJfTeTq6RhAEQRAEERQSpHEEC2MhlYSowqeFq7qqobtFEARBEAQREhKk8YRs+AwsSSULqe/y6rKSBu8WQRAEQRBEKEiQxhGyhTSIiVQKavLNCuWormzobhEEQRAEQYSEBGk8wYUZsme1g5kc1uqG7RNBEARBEEQYSJDGE9KQfRATqWQh5X0UqdNqbeheEQRBEARBhIQEaRwhDdkrFIEFKceE0+2BNwWUvYqCmgiCIAiCiC4kSOMJrnYUvS/SfA+YPO1y2E9P3wiCIAiCIIJAgjSOYJIPaRALqUK0kLo5XhakTjsJUoIgCIIgogsJ0jgk2JC9Uh6y5+XUT06ykBIEQRAEEWVIkMYRkoU0qA+pKELdnMc7fE+VmgiCIAiCiDIkSOMJUZAqFcqAi6W0T254BanT6To9fSMIgiAIgggCCdJ4QhKkysCnVUr75OY8srXU4yJBShAEQRBEdCFBGkd40z4FsZD6DtlL1lIasicIgiAIIsqQII0nRAupShVMkNa2kLo9ntPTN4IgCIIgiCCQII0jpKAmpUoVcLlsFVW4ZXHqcZMgJQiCIAgiupAgjSdkC2kQQSoP2bvlaZ4spARBEARBRJk6C9I1a9bg0ksvRVZWFjiOw7fffisvc7lceOyxx9C1a1ckJCQgKysLN998M3Jycvy20bJlS3Ac5/eZOXPmKR/MmY5kIVVp1AGXy4FMfkP2/OnpHEEQBEEQRBDqLEirq6vRvXt3zJkzp9Yyq9WKLVu24KmnnsKWLVuwZMkS7NmzB5dddlmtttOnT0dubq78mTBhwskdAeGDZCHVBFwqW0iV3rRPPE8WUoIgCIIgokvgsd0QjBw5EiNHjgy4zGw2Y/ny5X7z3nrrLZx33nk4evQosrOz5flGoxGZmZl13T0RijAWUjkZvtIDhVsSpGQhJQiCIAgiujS4D2l5eTk4joPFYvGbP3PmTKSkpKBnz56YPXs23G530G04HA5UVFT4fYjaiDFL0Oh0AZdzYgOPwls61EOClCAIgiCIKFNnC2ldsNvteOyxx3D99dfDZDLJ8ydOnIizzz4bycnJ+PvvvzFlyhTk5ubilVdeCbidGTNm4Nlnn23IrsYHooVUrQ09ZM8reTninmfs9PSNIAiCIAgiCA0mSF0uF6699lowxvD222/7LZs8ebI83a1bN2g0Gtx9992YMWMGtFptrW1NmTLFb52Kigo0b968obreiJEEqT7gUnnIXs3ktE+MDKQEQRAEQUSZBhGkkhg9cuQIVqxY4WcdDUTv3r3hdrtx+PBhtG/fvtZyrVYbUKgSNRAtpBp94CF7SZBCzclD9owUKUEQBEEQUabeBakkRvft24eVK1ciJSUl7Dpbt26FQqFAenp6fXfnjMFWWSELUl2iMWAbTvpfrYACwlC9h4bsCYIgCIKIMnUWpFVVVdi/f7/896FDh7B161YkJyejSZMmuPrqq7Flyxb88MMP8Hg8yMvLAwAkJydDo9Fg7dq1WL9+PQYNGgSj0Yi1a9fiwQcfxNixY5GUlFR/R3aGYa/0BnppDAkB2yhEwarQKMEgWEZ5KRKKIAiCIAgiStRZkG7atAmDBg2S/5Z8O8eNG4dp06bhu+++AwD06NHDb72VK1di4MCB0Gq1WLx4MaZNmwaHw4FWrVrhwQcf9PMRJeqOwyfzgE6fGLCNJD1VehU8ELIaMJAgJQiCIAgiutRZkA4cOBAsxDBvqGUAcPbZZ2PdunV13S0RBrutSp42JCUHbKOQg5604OEQ55IgJQiCIAgiulAt+zjBd8heZwwcRCZJT0Niopz2iTxICYIgCIKINiRI4wSnzSpMMAaNwRCwjXSytXqdHGVPlwBBEARBENGG1Eic4LTZhYkQLhOSBNVqNPI042jIniAIgiCI6EKCNE5w2G3iVGBBarPZwIniU6vTyWVE6RIgCIIgCCLakBqJE9yO0BbSSqs36Emr0XtDmchCShAEQRBElCFBGie4HE4AABdkxN7mG4WvM8gWUo6i7AmCIAiCiDIkSOMEpyhIgxWn97WQ6g3eWvfkQ0oQBEEQRLQhQRoneFzOkMttdrs8bU6w+NhFSZASBEEQBBFdSJDGCR63UHmJC+JD6nB4BalWrfEGNZGFlCAIgiCIKEOCNE5wi4I0WFCT0ylUZuIZg8Yn7RMJUoIgCIIgog0J0jjB7XYBCG4hdboEQSovldM+kSAlCIIgCCK6kCCNEzxuMZgpiCB1OwXBKoU8UWJ8giAIgiBiBRKkcQLPewAAXJDE+C5RkNZaSoKUIAiCIIgoQ4I0TvC4BUEazELqki2kwnKKsicIgiAIIlYgQRoneFjoIXspCp+XlkvNyEJKEARBEESUIUEaJ/C8aPkMIkh50YJacyn5kBIEQRAEEW1IkMYJvEeykAZZLgpSeciekRAlCIIgCCI2IEEaJ3h9QwMrUuYR5pOFlCAIgiCIWIMEaZwgj9QHGbJnYlooXpak4v+kRwmCIAiCiDIkSOOEcD6kTBzSZzVspIwUaVzgcjrx/mOT8NfiD6PdlQbH5XTiwycewk9vvhztrhAEQRD1BAnSOIHVtHzWRByy93A1o+wbtFvEaeL7l1/AMb0FKzdvi3ZXGpTty3/E7McfxSGNEZtyC6PdHYIgCKKeUEW7A0T9wMLoUTkrVI2h+iDNiUZGcXEpkJgEtyEBebt3IrNDp2h3qd5579EHcFxrBEwWAACv1sLldEKt0US3YwRBEMQpQxbSOIHJOjNYmL0wX/IhlYQpBdvHBw4xzyw4DisXfxzdztQztvIyvPDYwzhuSAKUPu/QHIeyE0ej1zGCIAii3iBBGifII/DBTJ689B/vP5+i7OMCj8d74vOKy6LXkQbgreeehlOfCADgPG5ksxPysmM7/o1WtwiCIIh6pM6CdM2aNbj00kuRlZUFjuPw7bff+i1njOHpp59GkyZNoNfrMXToUOzbt8+vTUlJCW688UaYTCZYLBbcfvvtqKqqOqUDOdMJ50PKeQThyUTFWrNgE9G4cftM29XqqPWjvvnwqUmoTkgCABirijHlySdw/cMvyctzDu6PVtcIgiCIeqTOgrS6uhrdu3fHnDlzAi6fNWsW3njjDcybNw/r169HQkIChg8fDrvdLre58cYbsWPHDixfvhw//PAD1qxZg7vuuuvkj4KQo+WDWUil+XLaJ46G7OMJj8L7U3bqE+FyOqPYm/ph79rVOMSZAI6D0unA+OkvQqMzQG80y29U5SVFUe4lQRAEUR/UOahp5MiRGDlyZMBljDG89tprmDp1Ki6//HIAwEcffYSMjAx8++23GDNmDHbt2oVly5Zh48aNOOeccwAAb775Ji6++GK89NJLyMrKqrVdh8MBh8Mh/11RUVHXbsc9LMCULwpeUJ7SkD2T/yfiAY+PVZSp1Vj/5ce44MbbotijU+fLpT8COgPAGC7q1Qk6g8FnKQPAwWqzRat7BEEQRD1Srz6khw4dQl5eHoYOHSrPM5vN6N27N9auXQsAWLt2LSwWiyxGAWDo0KFQKBRYv359wO3OmDEDZrNZ/jRv3rw+ux0XhPMhlUqFykFNnDSE39A9I04HvELp9/f2Lf9EqSf1wxsP3QuXKEab2ArRZ/RN/g1EC6nDzQdYmyAIgmhs1KsgzcvLAwBkZGT4zc/IyJCX5eXlIT093W+5SqVCcnKy3KYmU6ZMQXl5ufw5duxYfXY7LghnIeWkoCZxgnGhfU6JxgWrIUgrXI1XqJUX5qEkUbhHaG2VuHvW3FptpAIQbrp8CYIg4oJGkYdUq9VCq9VGuxsxjWTxDGbwVDDh3cNrIZXSQBHxAK/wf7d06vRR6smp89/vP8nZH26+7daAbRQ8D48S8JxBiUIObPgTyz79DOUKNdQuJya9/BrlYCUIIm6oV0GamZkJAMjPz0eTJk3k+fn5+ejRo4fcpqCgwG89t9uNkpISeX3i5AkmSOUhe86/hCgN2ccHrIYgdesNKDy0H2mt2kapRydP0fEjADiAMTTt0DVgG455AAAeLv4F6SdPP46jVTY4Es2AJQ0A4ASwZOazuO7p56PbOYIgiHqiXu/mrVq1QmZmJn7//Xd5XkVFBdavX4++ffsCAPr27YuysjJs3rxZbrNixQrwPI/evXvXZ3fOMMJYSPmaPqQ01hlP1BSk4BRY+UnjrGtfXl4Wto2CF16savrOxhulOcexDxo4TEmAQgGlzQpVdSUA4EC5Ncq9IwiCqD/qLEirqqqwdetWbN26FYAQyLR161YcPXoUHMdh0qRJ+N///ofvvvsO27Ztw80334ysrCxcccUVAICOHTtixIgRuPPOO7Fhwwb89ddfuP/++zFmzJiAEfZEZIQbsuekIXvJQqrwWkjjIUXQGY9kKWTeF42cgsaZEsnhcAkTLPhLk5IXLKTxLkgPbPgbUCgAxqOlpwjN9GVgBh3AGJxGM5a+9L9od5EgCKJeqPOQ/aZNmzBo0CD578mTJwMAxo0bh4ULF+LRRx9FdXU17rrrLpSVleGCCy7AsmXLoNPp5HU++eQT3H///RgyZAgUCgVGjx6NN954ox4O58xFjrIPslxRQ5DyCu/D3l5WBnWNQDOi8WCrrJB9LlP4YhQrU4X5ysbpX+gUq05xLLiHs4KdGYI07/BBcYrDYfG8+v7Id54oxOWnvVcEQRD1T50F6cCBA8FCWC44jsP06dMxffr0oG2Sk5Px6aef1nXXRCgkC2kQRaqomfbJxzZur66AESRIGys5u/6Tp1O1HhSLZZuchgS4nM5GF/jilos8BBekSlGQsjj3IS0qKAA0QnGABFaNJspyJCYkYGuFEeAUcJiTsPydN3DR3ROj3VWCIIhTIr7v5mcQYS2kqGkh9S5ziD5pROOk6MhheTqzWQsYmVA4gqk12LL0iyj16uTxcILVU/ITDYQS0nUc37ewKptY4Y4xPDDlWYx9ej6ueOhVtFfny2027T4Qpd4RBEHUH/F9Nz+TEE2jiiAmUimoSa7Q5DPSaa+kyleNmeI8rzjRJlqQpvC+YPy7fkM0unRKSIKUE/1EA6GWgvLi3ELqlA6T56HReStVjX7oFRhdZQAAh8mC1QvfiULvCIIg6o/4vpufQbAwQ/acZCGVgpmUXrcLh5XKLzZmqnyi0o2WVKQkp8h/lzmDi7pYRfILDWUh1aiE65kFu+DjBLdkLWb+51GjT8DFFw8DGA9wHP7auTca3SMIgqg3SJDGC+JzmQtmIZUT44sPeZVCzk3qtFH6mMZMdZX3/BnTm6L70Gvlvx2GhGh06ZTgRRGmZO6gbQx6MfF/nAtSj1Jw81fytb+Ljv0uRrq1EADg1Cee1n4RBEHUNyRI4wQmKlKFIvADWilH2UtBTQwKcR2Xw34aekg0FDan9/wlZWajWcezYebLAAAenQF/fPx+lHp2ckgWUmWIIXtTklmejue0ZbwkSD2BxXnXnj2FCY5DeWHg0ssEQRCNARKk8UI4H9IaQU1QQBakDhsN2QPAkhnT8PzDk7F83mvR7kqdcHnEc8oYEpMzAABpKq/VdPP6zYFWi1mkJP+KEIVtM1u2ESY4Dvl7d5yObkUFyUKqZq6AyztcMFSe3rVm+WnpE0EQRENAgjRekAVp4FMqDc8zKTG+kgMnWUidjtPQwdhnd2EZXIkmbNm5P9pdqRO+aZKUKkHApDdpJi+vamR17aVUTuoQ1cSadOwuTx/d+V/Qdo0dJlqL1QhsIU3Lbi0XEMg91LiuW4IgCF9IkMYJ0pC9Uhn4lErWUCmoifOxkHrieMgzUgoP7Ycz0QQAcOoNYVrHFlLqI9+8neePvgcqJpxXd4IRW39eGpW+nQxSoJImiPsJAKQ1ayVPFxw/2uB9ihaStVgXQf7/stKyhu0MQRBEA0KCNF6QLKRBKtdIQU1yDXuVwjtk7yAL6c/vvSOUaATg0elx9N9NUe5R5ASKSk9MTkcW5y0d+sfPv5z2fp004rWsN+iCNtHoDLJlsKK09LR063TjtFrl7yLBoA7eUHwRsTkDD+sTBEE0BkiQxgniiDyUymA+pKKFVByy5xTeIXuPix5keZU+mQY4Dn98/VX0OlNH+CCpgTJTU+XpCrX2tPapPjAlWUI3EAWpzR6fL1THtm+RBWlaZkbQdpz4Pbg8wV0cCIIgYh0SpPGCZCFVhrGQimecUynkeR5X8PQ6Zwr2BCMAgHML4jy/tDya3akTvOimUTMqvfcVt0Mhpk5yJZqwf+3q0963ulJ49KB8LWe1OCtkW0mIOT3Bg59igeO7tmDdknfrvN7R7dvl6bTs4N+FQjzvUs5SgiCIxggJ0jhB8rtTiUEtNallIfUZsnd7Gl/y9Ppk9YfvgNfqAJ6HqbwEAGBvRBZFOU1SjcCXlGZt0EQatuc4LP8i9q2++9avkafPOu+CkG0ln1k3C+5rGgv8/Pl8LPvvBFYsmFmn9QpPHJenU7PbBm0nVbTyBHHXIQiCaAyQII0XJEEazEIKfwupUq2Qh+zd7jPbQvrv5n8BAOrqKrRqngUAcBkSG01+SykSW8lqv1ikm7wBWmWN4OdecPSwMMEYLFnNQ7aVBKknxsuHVjHhHBw4crhO65WVi1Z6xpDeqkPQdpJl3MMFfhklCIJoDMT2nZyoA2KUvTq0hVQyJilUaq/V9Ay3kFaJ1tAEpx2Dx90J8DyYSoU/P/4gyj2LDCkSW4Xa57HXxTfJQS8Ooxl5u3ee1r7VlfKysojbKhqJZdABDQDgBMvA7r+WRbyeTXpRZMyvjn1NJEHKx/j3QBAEEQoSpHGCPGSvCTzULOchFWvYq9Qqb9qnMzgYIv/AHjhF/9HOXTrAlJEJta0aALB7W+NIuB4qb2ezDj2RAaG8JBQK/PD+O6eza3XGLlmlWfhrUiEK7VgWYh6XE3aIv0mOw5YVSyJe1yWeV0WIilWAt6woH2R0hCAIojFAgjRekHI3agKnh5GG7KX/VGqVPGTPh3ngxTM/vzcfUCigcDow8NZ7AAAau1C5qrKxCHXx3GvVgX0pMxK8VvMiZ2wHADndYp5cFr6fklDjYziYp+jofvn8AMBxtwW2ysjSVHkUwnkLK0ghfQ90OycIovFCd7B4QXzoKTWagIslayjEtFBqjdbHQhrbIqUhKagW6sDrqiqhFr+7JJ0g6p362K9wVJGfJ597gz6wdbzb4NHytCfI9REruFntJP/BkIaqJZeFWKQk5xAAQMXcSGDVsHIGrFj4YkTrSoJUxUL7eGvEn3Ysfw8EQRDhoDtYnCBVatJoAycTlwWpeMbVWo3XQhrBwz8ecTmdsCcKw/Wpeq9lueeFQnS3W2dAzs5tUelbpOTs8aYGMpnNAdu0PWcg9K5KAIBHHduC1CNVneIjEKSIfd/J8sIcAIAOdjRXC+fgWHFFROvyYh17JR86T7BOI1rAyUJKEEQjhu5g8YI0ZK8LIkhFH1JOzFmp12rlPKR8BP568ciaD98FrxHSPY0Yd5s8v9dl10DhdAAch9VffBbFHoan6Ji3bGZicnLQdokuwS9WEjmxioerXXUqGEoIbVgMCzFreTEAQA0Xzhl5PTjGIw/p2LIs/HUl+YSqw1hITWbTqXeUIAgiysTunZyoG6IgVQexkErWUE6sD67V6c74IfsdYtCSuroSWZ26+i1TWwUBl1cc22UpywoL5OkEU2rQdgaNaEWM8WFdbxnU8H7NavFQWIia99HGVlUGACiFBYc2/oNmXD4AYMeGlWHXlb4LLRf6u8jIbiVMcBzKC/NOvrMEQRBRJLafTkREuJxOr4VUHzg9jCQ+OZVwyrUavc+Qfew+0BsSm2hZ0ztrl540iJHLthhPkF9V4R3+TUwNXl4yMTFBnnZarUHbRRspQClQTtWaaNWiyOZi9/p1igFy4Dhs2bUfrVoI4vGYJxUlOUdCryy+POjVoW/THfoNlqd3rVl+8p0lCIKIIiRI4wC7T+5GnTExYBvpkc2JQU2JiYne3KQN2bkYxi36U8rWQx+aNUkHALgMCXKC/PwDO7Hqo9nwxFAhgaoqr7g0pzcL2i4tK1OY4Dic2PlvQ3frpOEVkaU6AgBDghR0FsOC1OUtrmAzJyG7Y1+YWAWcnAYbv18QdL3SnOOy0DZaAv+mJdKyW8tpsnIP7a+HXhMEQZx+SJDGAfZqr5VMa0gI2EYSnwqVIL4MOoOPII3dB3pD4hbdG5pkptdaNnjcHWKCfDXWfbEIALDs49ew6mA1fnzz8dPaz1A4XN6Al6Qm2UHbpfvUhc/Zv7dB+3QqyFWnEN6NJCklTZjgONgqIwsUOt04nT7CWqHET59+jhRFFQCguDA36HoHN62VpzObt4x4f2Wlse1iQhAEEYx6F6QtW7YEx3G1PuPHjwcADBw4sNaye+65p767cUbhqK6Up7VikveaKGpUcrIkWLyC9Aw0kR7f/g+YaCHtNmhoreVJWc2gsgnWx+2btwIASnnBHSKnPHaGvF1S8A/jQ1bzSWraUp4uPHo0aLtoIxV4UEcgSJu0aSdPx6rV1+ryf9krNxhhFNOJVbqD337zDh6Qp1OatQ6/IzFThs0ROiKfIAgiVql3Qbpx40bk5ubKn+XLBZ+ma665Rm5z5513+rWZNWtWfXfjjMIhBuAAQIIlcKS15GanFC2ker1ert50JqaL2frbLwAAzu1Cq3P6BmyjtwvCs0Shgq2yFOUQopnzkI69a389PR0Ng1s8seHSJKU0ayNPl5aVNGifTgUpYl6jCn9NNuvUU54+vmt7iJbRw87EdGK8B/B44NHpUVwivAEWsyQ47YFfbooKCuXpjDadw+6HE98qXWdmfCJBEHFAvSuRtLQ0ZGZmyp8ffvgBbdq0wYABA+Q2BoPBr43JRGlLTgV7ZZU8rU4I7G8mWUNVam++TU62kJ55Q/a5x4XhUqWjdkCTRJ9+vQHG4DKa8eOc2X7phbau/KZW+/KCE9j110/139kQSEFAijC5ZDU6g2xFs4Y45qgjCmydLny+VHN6U9m8X5SX06DdOlmcYh17hdsNQ7kwnF5kBzTMCSenwbbfvgq4XpVdKNgAxmBKbRJ2P5LPrTuGq1YRBEGEokFNY06nEx9//DFuu+02cD6RsJ988glSU1PRpUsXTJkyBdYwUb8OhwMVFRV+H8KLUxxaBmNytaGaSN++2qe0qFz6PIajlBuKKnFoUxUgwl6i35hx0Ii+iQcKq/2W5Tq0fsFNTrsVn8yZgS9+XYety79sgB4HRk6TFEFxA44XrWiNoFJssCT/vihV3pyqFTHqQ+rmhN+bwu1CpzbNAQAOswVGj9Dfo7s2B1zPIZ4rjuf9jjMYnChIPTFcJIAgCCIUDSpIv/32W5SVleGWW26R591www34+OOPsXLlSkyZMgWLFi3C2LFjQ25nxowZMJvN8qd58+YN2e1Gh8vhtaYEQzrRKl9BGmDqTMEpPrg1YaK5m4h1GW0JghW/GXKhZG6UchZs+eljud1Pc55EAZcKxilwYndgkdEQ1CVvp1SO0xOjLyCFRw/KL0eZLdqEaS0iXvMOZ2z6TrrF8p9KjxuXPPAYVNVVAKeA1SHML6+2BV5PtnxH9vYglVH1cLFd+IAgCCIYDSpI33//fYwcORJZWVnyvLvuugvDhw9H165dceONN+Kjjz7CN998gwMHDgTdzpQpU1BeXi5/jh071pDdbnQ4bOJDLYQglSSIxievpjRkfyb6kLpFS3KCJvQDfMyT04SqTaLwM2kUaMoJyej3bv0bAFB4dD/2VniFvt1aVXtDDYQkSCPJ26mIcSvagY1/ydNtzu0X0Tqy76QnNiPzpMpTSo9gTU9xCxZ5u1bIhlHGAgeieZTSi0ZkKcYkQRrLZVQJgiBC0WBK5MiRI/jtt99wxx13hGzXu3dvAMD+/cHz52m1WphMJr8P4cUlDjtzQTKKOp1OKGTfPL08n/NmJ23Q/sUibo0gzDOb1E755IveaIKxskz+25SciqaZQk7PHI8ZTrsVyz+cBSvnFRYOp7PmZhoMJubtVIYpLwl4rW0eRWxa0fKOiC+ljAm5NUPw39adyMsvkq2+7hjMYGerLJWT26tEwXjtg5PBud1gKuEFpoyzIHf/tlrrSiVeVZ5IBalbXI8EKUEQjZMGu4svWLAA6enpGDVqVMh2W7duBQA0aRLecZ8IjEcSQEEspFaH10dXp/OxkJ6hPqRH/90kp3zqPmRY2PaDRgyTv9uSfAf63/ggdMyOai4BP745BQfcQoWkdCZERrv402etk/J2qhDeQqqKcdFSUV4eUbst67ci6bMiVL+y3Wv1jUFBWnBojzytFn9jKdmtoBYt6CrRWrp95dJa68qClEXmiiBZyPkzcLSDIIj4oEHuXjzPY8GCBRg3bhxUPg75Bw4cwHPPPYfNmzfj8OHD+O6773DzzTejf//+6NatW0N05YzAIUVNBxGkVTbvELK/hbT21JnAv78Lqcg4twste54Xtr0h1SiL9mPFVuiNSchSFAMA/qsyw8MpkYV8ZBkFq5eTnT7BJ9Vxj0SQSlbUWB3WtdtDv1hJ7PlzFziOgwYK2UIai8dUmuvN95rg8yKolao3eYS+F+XXdkGSjkeDyCykUrExyWJOEATR2GiQu9dvv/2Go0eP4rbbbvObr9Fo8Ntvv2HYsGHo0KEDHnroIYwePRrff/99Q3TjjMEjVuvhgjzIbVZv4ESC1sdnTc5DemYJ0twcIeWTSgoGC8Ox7evlaZs5CUf/3YRW7boCEPJmcoxH774DoDNaAABOdvqGxKVUVOoI9JhKtKKxGBRvAOB0CwKNC5MxQGET23EclHzsCtKq0gJ52mT2uhlZDIJ1XrJmBkqQL50jjTIya7tOClYkCylBEI2UBrl7DRs2DIwxtGvXzm9+8+bNsXr1ahQXF8Nut2Pfvn2YNWsW+YSeIh6XZEUJMmTvk3xbr/fmKT1TLaTVYsonZYS+nqViiUfO4waUSvywcCHOv+Y+mJiQuqe1Mg/dh4+BKVnwR7VDG3Rb9YnL6fTm7dSEF2Qq0UeDxahocYm3o2AvVhIqHwGnYsI0H4OWQWt5sTyd3qyZPN3lnHMAALzoNlLMLPD41Lz3Pa+JEeRjBQCTKXCFNoIgiMZC7N3FiTrj9giWr2APcofTayE1GgIkzj/DLKQOMahHE2EEs5RIXusQcpFW8ByUKhXObtMEbRU5GHHLowCA5CwhEMcKvV+O0oai7MRR+dwZ9Lqw7bUqsRCCIjbPt+QHyoVJYaX1eC3QagiWwVi0kJaXeCtitezurSp19uXXgnO7AY6DknfBwWnx7+9fy8uP/7dZPq+pGWkR7StDSpPFcSjLoSwkBEE0PkiQxgFuSfwEE6Ri1RfGGPR6Hx9SsTk7wwSpR0z5lKhV+83fu/ZXfDL9Lhz5b53f/GpeaJfIhO/RaRBS9gy8+RGMfXo+0rLbAgCatO8ubJ9TofjEwYY7AJH8/d6gGUtqatj2iQnSuecEK1yM4c2pGnrIXucjSDWiII1Fq29ltde3u0lHr4+8WqOBSixmoXYJbY7u2CAvP7rjP3k6rWXbiPbV4fyB8vTOv34/2S4TBEFEjdi7ixN1hhctpMEEqU208NV8zJ+paZ/cWsGa2CTLP7PD379+iX18FlZ9877f/EoIw6HtzmoJMAZeq8Pm72pXYzKnNoGGCUIv/0DD11Yv9MnHa0wJb0lLzRSyAYDjUHwweJq1aOGJMBm8lvkKUsE9IhaDeeySkZzna1VQ0zjFl0QxI0N5lTfwMP/4CXk6tXlkgjQtu7X8+889HDynMxE/bP15KVZ+MC/a3SCIeiP27uJEnfGIScG5IK53LocgkmotPgPTPh3+Z4OcA7L7kIv8lpXxgjtDDp+KqhIhICVn77+wcoJl8dzLbpAtW5tW/xFw+3oI7hGleUfqv/M1qCgpkqcTk8IL0ozW7eXpE3t3NEifToVIq075ClIthJeLWLSQOnmhn4GOxyhGobnFykplfIK8rLysTJ7ObN2pzvutKIssfRbReMnbvRNL/9yA1Ufz8Nzjj+LVCffiz08+iHa3COKUiL27OFFnPJ7QQ/ZuVxBBKnHm6FH8t+I3AADnciG7+zny/Jy9/6KME+qnOzgt/vjsdQDAgc2rAABGVomkzObQiAFi5UEKwusgfNfVpUUBl9cnleXe+u3G1KwQLQXSs8+Sp3MPHWqQPp0KvJzkP7Qg1cDrL6rjRN/ZOrxU/fjGbPzv0Ucw78EJde9kHXBJdew9tY+nTXvhXEjuI6WcBfkHdgIArGLWDPA8NPqEWusGRcxOYHPEZhlVov74dv7bYGrh+vLoDChPycBv+47if48+jNcn3oetP9fObUsQsQ4J0jiAF4f9glVqcosPOL7GcukRzs4gRZqbkwcAUDn9Uz5tX+1/Az9RKAjK4hOCcDNyghBN1goWLUcQoaDhhJcDq7WynnocnGqbN1gtOatV2Pap2W3ll5bSovwG69fJIledCuNDqvYTpPoQLWuTs3MbNucWwW1IQJEmfCDYqSDVsVd4agvEC264BeA9gEKJBLdwrfy95B0A3mwDkdaxl5DLqIb++og4oER0b0koLkR6eRHUleUAY3AbElGanI5v123B8w9NxkdPPhLlnhJE5MRmDUGiTvBS3sYgFlIpLVRwC+mZI0irnYI4UNUI6inOPwGgCdJYEQq5VOSwdOTs/RdVVisACwwKQRz0GTYMX/21ER69AfvXrkbbvgP8tqPheIABTqlYQQPicHt9hxOTwgc1KdUaCFcBh2pbZDlYfflr8Yew+lRTSm7SBL0uu6bO2wmGNOyu4sIIUqaQ36b0EPPqchwq8vNgysgMue6id94BL35Xbp0BhzatRatz+p5ax4PgEV0QlAGG7PVGE1Q2G9wJiVDb7UCiEcerGDxuNzzK4EP9oVDwHniUKri52Ms4QNQf25f/CGeiMJpz3jndMGDc3QCA/375Dqt+XIYKrR7uBCNcRhMOetxwWq3QGAyhNkkQMQFZSOMAyUIaVJCKwoWvuVz2IW2gjsUgziApnyrE3JZZiUqksBLwnBLrl34AGy/MTxRv6F0uGgWlXbBM/rG09rCYWiWIAZen4c1U0hGESyTvi2RFc9Shf7bKCsycNAHLdx/CX7kl8uf7LTuwcMpDdelySCRBqlGFviCVPhesDl4r5/Ht/4Zc792HH4AtKVX4nTAGcBzWLPnqFHocGl4WpIFTgGnEwgwuJ6BibhRzyVj39TwfIVu31GFyGdUYTIFF1B8rvv8R4DiorFWyGAWAbsMvw8Q35mLq7JdxUYdWAM8DShXWffVJFHtLEJFDgjQOkIRmsCF7XhKkQdY/k4bs3aLPXoJPyien3YpilgQAyO7YC5l64fvKq3KjkglD88lZLeT2GpuQj7SouraVUasRor6drOF/Wrxo2Q6XSN4XThwOd0f403darXhj6pOwW1IAxsC5XeDcLkD0izzOK+ovhZSc5D90MnhfQWrwKUJwXPTBDMSGrz9Fjt7o3Y+4r8JKW9B1ThVetFSqg7wwGERLsFOtQTOF4EKxb9e/4JXCtak6WUHK0cBXPFOhE16OTT4FT2rSb8w4qMQX573bYi+AkSACQXeuOIBJgiSILpEEKavVQKrc00Adi0E8YsqnrGbeIKDtv38DJ6eBhjnRdejVMDdpgR3frUA+541cb93TOzRvUnCwAXDoag+D6RJNQDXgZOpay+obOU1SHYZ2FcwDHuqIrGgupxOvPf4IbMlpAGNoZi3HHbNfg8ftxq/vzcb6HBvc+gR89uyTuPn52Sd9HDUxWywhlyt8BKkaStnaWVKQL/f7o6cehdXmFcrlWgNYQqLcVsJel6ChOuARE98DgE4d+DbbvFkTFFV74NYb0KZdWxzeU4hjLAO8SvhdqlndgpMki2osFgkg6ocVH8yF2yBcx0MuvyxkW7XDDrchARX22Ms5TBCBIAtpHCDp0WCWMt4tzK8Z1CRzhgjSQ5vWyimfegwdLs8/umsjACCFK4VGZ0DbXgPQhPPWIdczG5p1PFv+u1tvITrfbTAgb7e/Vc6YJOT6tCOyko+nglTvvC7BL5EO67qcTrz20CRYRTGaWVmCvpcMwufP34O3nnsY6/McgBiEdMx56u4JZTnHZAGXnt0yZFvfy1XNvMdRXSVYrj979kkc01tQnJwuf9w+YrSN4gSSXEJZT7fegMJD9Z+TtbzghHw8SWZzwDYXXDcWYAxMpYZWm4xUVgwPpwITfUg1XN18SCVfVV5JgjRe+ecfIb+xprIcHQYMDdlWJ94XHKqGfzkmiPqABGkcIPuGBhGkzCMN2Qf2IY18wLdx898qoYIN53KiWRdvKcfyamHoy6jyCqtMk9f6aYI3aTkAnHfV9VA4HQCnwIrPFvktS24iDO3bULfo75PBcxKCVBYtYQTpe489hOqUdABAclkhtCYHvlz5D3a5MlHKWbz7ZAyuBCM+f27qSRyBl30b/pSnz+rdP2g7q9V/mFIFTr7uHWIqrhyxQpLSZoWmuhJKp0MWoy25E7hhyhyk60SrEcfh90X1n78x7+Auebpp68DJ7VOyW8n+yNvXb0Azk0ruE+At9RopUrosPgZzshKnjsvpRLXRBABIhgfvPPcAXntmElZ9FHh0ItUitHXpGv5eRBD1Ad254oBwsUnMIw3N+0tPeQj/DImyz8sRhnRVTv8I+DJeEJ+pad5h/POvvhcqJgyBJij8h7zUGg00VsEal1fin4Q88yyhRKSLU6Mkp2GT47MQUdw12bprK3586jM5Kp1XhPbWKRf9OPTlxXBaDDiCLHCMRxbycba5CvfcdhMyWKF87Rwoqz6VQ0HOkX3CBGNC1aEgHDmUA87nelWCk4O6XAw4+u8m2E0WYaFWA2eCER6NFuA4tEAObnjsdSjVGmS395byPFFYWqe+Ln3pf5g1cTy2L/8x+PH4FB5oc/Z5QdupRUFaZnWgTfchfi+VTbLCZ07wRSPezWOxahVx6ix96XnwGh3g8SA5jUMBl4YyzoJVB6rwyfS75GIeHpcTqxe9BBtfAgDgNVrk7NwWza4TRESQD2kcID/DggW38NJ/NZaLz/UzxUJaJVrQfAVp/oGdKOUsAIBO/S+V56dlt0UzLh+H0RSmxMRa20rg3bADsGm8QTUblyxGSX4uVMwFN6dG3r7//IKh6htvIvnwwS/bvl2LQa4ucHBWlOBYWNEiBX/ZzCkAADMrx3md26LftdPlNj2798Cy/4Qyl06jGd+9/AIue+iJkzqWitIyAKaw7Q7vOYCu8FqvFaIgZQDc4PDDwoWAWRByHoUKJlaBTHU1OvTqj7NHTpPXO/fSW/Db9plgCiXsAXyBg/HtrOewtdIJJKdh2Xc/ostFowK2KzhxQjgexpDZIXi1JT3zyNfRd7+sBBKM8jJLRtOI+wUAOilQjyykccnBvGIgORW6ijLkKFQAB6SwEhRzydjHZ+GD159HstKGXI8Z1VwCoMqSRwbWf78EV3bqGu1DIIiQkCCNAyRLZ1A7pyeYD+mZFdTkVAmXu84n8fr2Vd8AEASXr58oAFx211Rs+H4BBo97vta22rU/C2sLy+FKSMSbE+9DhVYPV4IRYAw63gW3Uo2S3Ia2kIp5O4PmT/CSXiUEaFm4RL91g+H2GeZrzeXgkruerCWu+1x1J3ZvuxOHmSCcdhzPR+gwi+DYbA5Aj+AvVSKVhZWAjyDlACh4HjyEqPZSnxycHTX5uPrRN6FU1b7NafQJsHjKUKpIgUufAGtpCQxJySH3/dObL+PfCgcgbs+eUPtFRaKqyg7oTWGPJz3ZglIALqPoZ+pxA6IPaXKTliHXrYnJZAQaLmkAEUUObPgTNrMFAGDUuFHIpUDH7Ljhzgew6cdF+DfHihIuCSV8EsABWuaAG0o548KJnIIQWyeI2IBepeMAKW1TsKAmKTaipiCVhOyZYiF1awWRlWTyRlYX5h4DAFi42kPOyVktMOLuadAEsKANuvVucG43oFCiODldEKMAwHFwi7lLK0sbthqSN5F8+DNo4oVjNzCt37qBOLRpLSC6A7RBLm5+Zn5QS++AK26FkheiwR3mJMyaOB5fz5hW51RQTrcgqsOlsHJXiGVwfdpJ/qwupQauRMHKqmQuDLz+gYBiVKJJgvjDUCiw/P258vxFUx/B9CmPY8akifjgsQdRmnMcv787BxvzS8B8tufW6mCrrKi5WQCAXXwJ5MJUnepzidcqD48HPYxadNYUoJ0yBx36jQi5bk0yWrQRJjhOCBIj4oavP/4MTKmC0m6F2yBYwltqypDSrA2G3z0NY669Bi25E2iBHPTN5DFh0kPI4Irl9a1nyk2eaNSQhTQOYAGm/BsIgjVYlP2ZYCEtzTkOj1YQY53OPVeeX+EWDt6cUDfHf43BAH1FKazJaVBZq2G0VcMDhoqUDDkdk70qsFipNxRS8Ev4plomNJLzdobwG962eqU83axV4IAciVbdz0f7Hxdhp1PILmBNTsM2B7D9uenQ2ypx1+SHYclqHrZ/UrnMcEn+lXbvSxQHgOM4KMRVnPoE+biyFQXIaNUh5Lbadu+HnRsF39VDRwTXg83ffYmD0IJpVXBodTgK4I2358nR8H5wCqxc+C4unlC7OIAbkVVbanVOX6gXfwmXPgEd1TyuePSpkO1D0eH8gfh990cAgJ1//Y7zr7nlpLdFxA6/vzsH1iTBdSbFU4kCLgMa5sSA68bLbbI7n4tbOp/rt16i0gOIl5/Lx7WIIGIVEqRxQLiXX6kSY9CgpjOA9Uu+EHzrPG50v/gKAILzfzGzABzQ9Ky6+1dNmjkbOXu2o2VPIWjlr8UfYvnuQ3LaHoez4cqHChZIMc9lBIpUI9Z/1zMxHVWIUpu5ObmAUXgANu8SPCBH4uJ7nsWx12eikvP6gDKlCtbEJLz3ymw8/NIbYbfhkQRpGIui1iUchxsMaiYIUg1Twi4ek0SH7r3D7rP78Ovww7pp4JUqVGv1cFqtWLZ6LZjRx5eVMfl8AoCCudEr1Y2NxUI+2wP7DwTctlvOERvev/fBJ6ei5MQRv8wPJ0NadmvZZzD3cOB+EY2P9bv2AyYzNBVlgFH4nbRQFaFJ29D3rKTUDEAcpHHrDXA5nVCHKToRK1QWFOCPzxeB+bygtu7WHR0HDotir4iGhobs4wA5yj7YkD0vWUj9H/aSZZQ/AyykRw4dBgCo7Db5prx9xRI4OC1UzIWew6+v8zY1BoMsRgGhOorCaZeFkcsdeTqmulK4f4+8H5MlcJ5LXzRivk4t57XyHdu+NWDbKqnfPI9W3fuF3XZicjoG9emF9qpctFPmohVyoLMLqbKqEpLwx+LwaZWkNFThLIpaXhCHHp9rWV3jvTrJU4LeV9wRdp9KlQpmdxkAwGVIwNzHHobL6B3yNzCbn8jlmAd9m+kwasJMKDyCm0KVKvAD3i2KWKUn/DVgSEo+ZTFak4qy8vCNiJjnwycegdNkBnge2WYhsl7J3Dj/slvCrntWr0HChPhStfWHJQ3b2XrkzZkvYENxJTaWVMufz1f8gQ1ffxrtrhENCFlI4wLO598AS0WdWnPInhdNp3Wxk77/2CTku4AElxP9hgzEOVdcW8e+Rocqp2CpUvtYLQ/v2ADAhFSuFJp6qtijra6CTSNYz2qWD311wr2wqbUw8S6MvOF6tDnvgpPeT+7+3fK0KSUtREsBlfjuqQAnj3ef2L8bnYfU9lN0iiJL5XGG9MH05eyRN+LskTfKf5cX5uHVN+cACiXWbN2FC8eEXl+uOhUmp6pGFKQujoeWCUVENVDD9ypumRT5bS3LpECpA4BCibJk7/fY3eLAiHuexR+fvY5jxw6hnE9AhyYmXHSnkG81wVWFSmWS4CYQACmtluRfe9pgPMApYXOc5v0SdaKyoADvPjcNDrUag/uei97XjA3Y5qjoj55QVoTqZAbAjJaKfLTqfn7YfbQ6uz8MPy6DlRN84Hds3IhzrwrzQ4wBnFYrnImCTz7ncgFgYEoloFDir1V/4LzRN0S3g0SDQYI0DpAsnUEFqWQh5QJbSOsiSXOZCm5TIpwAfvhnB375czIsbgdue24G9MbwaXuihVP0/9P7DAFVVFUBMMGoqFvN8FBYlJwc6Oxk3p/Xn598gPLkdIDjUARg0Q+/QrP4K2SqOdz03It1Hkorzcvz7jM1I2x73/rvHDgwMBTl5gZs6xb9zbQee5365Is5LRNNnaU4oUuFS2vAx88+jLHPvBS0faQWUp34nbp9Xq80nBYQBu2h9LgwcGxtn85gdLlgOHb8LlTqkqyhzVgORk2YC6VKhSG3BU5jlaqxoxIAU6mxf+1qtO07wG+5dDwqNJyVPBAcz4MplHCdevEsooH4bf6bWLf/KNxi4YlftmyHNnEpeoy83K/dB/+bBk9yOji3C21amPBfpQ4K5sG5F42OaD9KlQpJXAWsYlaK0iprmDVigw3fLBaCKhlDR0MpFEoFjhYBFYmpqEowNSrXA6Ju0JB9XBDGQioP2dfwIeX8/48EXhR2SrsV4Di4Ek0otKThlenP4s9P6r/iTX3h0glWyxSzN8+jjRdEQ0JC/dUz7zfSa3GsZjp5et26TQDHQWm3QWmrBhQKOE0WHNWb8dKjD2P/2tV12s/+3Xvl6YSU2n6gNfH9oUuJkSqrAyez94gWUj07NR/YO2e+BaXownDApYXdGvyB6M2pGsZCKgpSBzzwiNezzqcqVnNlAczpkefv7HjhKCjd3owACXwVLr1lcljLcPPW3uT9f//wQ63l0vFoFafXT1sh+uC6udCVuIjTj9NqxRsT78OfxwvhNiSAc7uhcDrAa3X4fvVf2LXqVwBARX4eXppwH0qTBIt9cmUpjlUIL1ytlfl1yr5g9InDcygbRwnRvf+JSfw5Djtd6dhuT0VFophbWKfHz28Gf7ElGjckSOMAr4U08MNPIQ4d17aQSiVFI8PldIJXC2IlS1mBEZ1aw1xcAHg8cCUY8dvuQ5g7aXydU/40NMVHDwkVTgB0Pt871FXJBMtBSlbLettXl4tGQeEQbKRWUSiV5hxHtTkJANCUOfHUi7PRL9OChOICgDE4LMn45Idf8PFTj0a0j+Kjh1Cg84ropMzQUew2m39ySqV4PTgD+DeW5hyXa9QnJ5y6qOmRaZQj1N99KrjlkkUoSLWinHZybtmPNFFlkZf3umB4nfuYyRWJneDRp2PzsNH5ANB5gNeaVVARQNiLabUM2tNryZFcHjxhSsMSpxdbZQVeeeJxlCSnAwoFNJXluKzP2Rjc+SxwLic8OgO+/nk5vnz+abzxysuoShFGU/SlRcjI4FDKWaBnNlx00+Q67Tc53Vt9Tnopj3XKrN6RmWzkoAVykMq8Kaz2HD4RjW4Rp4F6F6TTpk0Dx3F+nw4dvDd4u92O8ePHIyUlBYmJiRg9ejTy8xs2X2P8E9pCqgiS9olXesf6nSGsVxIndmyVhzWPqTOxdec6jBp7FYZ3aQt1VQWgVKLAkoaXHn04pkTp2m++ADgOnNuNzkNGAgDKi3JRCSGxeYtu4f2x6oKuWgjo4RUqlBfl4vOXXgRTqaFwOjDmcSGtz0X3TMIjb85Fz0QVlHYbmFqD/UoDXppwX9jtfzR7tiywwRgsmaGrQR04tt+v3KZKFKSuAKbxLT8ulc9xy46nXtnl0olPQm8T0l8VJ6bhxO7AJQzlnKph3EckX1g754ZbfMEyMCOSWBlacyfQdejVde5jx87doGN2dNEX48LrH4honYxWHaB2Cw9Ou94/T63vtZ+SVrfyn6eK5PIgJUQnoo/TasXrU5+E3ZIM8DxSSgrwyIwX0fOSq3DBjbfhwjbNwLlccOsTsMOlgFufAM7tQrPqMtw0/k4cdAijOu0S7RG9LPnS4fyR8rRHq0fx0UP1emwNgUMtuAxxvAfjps7FrdPmY+iwUYJ/NACr2QJraUk0u0g0EA1iIe3cuTNyc3Plz59//ikve/DBB/H999/jyy+/xOrVq5GTk4OrrrqqIbpxxuC1kAaWpBwL7EMKn+FER1VV2P0c+meLzzZ55CEdX//0O4rzduCBJ6fCUpIP8DwclmR89/ILdTyKhuP4keMAAJXdKvseHVi/AuA4aJkdTdvXb4RzqsGb8y9/3zYUK4V9JlaW16oGdPkjT+GuO2+DoaQQAFCVko6vXngm6La3/rwU5ZYU+W8twgceHdrj/xCSIu49AYZ1D+3zugK07zs05HYj5fLLRonpiBT44t15AdtIglSjDO0/opasuwo3XKKFVMs0eODZ13DzM++eVP8uGHM/Hn92Jq5+fE6d1jN6KgEAbp3B74XuwIY/ZVHfqvvZAddtKJRimimeLKQxgcvpxGuPPwp7UgrAGJo7KjDhjbl+PpCDb7sP52YmC4U2AGgqynDlhb1xx+zX8Pvit2DndEhmpRh1f93vqdmdz0UiE65TcBzWLvm8Xo6rIZEsuXqPVb63deg3AtkQfN6ZSo2vX54Ztf4RDUeDCFKVSoXMzEz5k5oqWAnKy8vx/vvv45VXXsHgwYPRq1cvLFiwAH///TfWrVvXEF05I5ArNQV5lgezkPoayKyVpWH3k3/sqDw9uEMq0lkRHJwWm0oN+OL1x3HdxPugtgrCtqiwONhmTjtSDXu1j+Uq//BOAIAZVRFHkkfK0OtvkEtG/v71t2JJUR6DLw48nJzRpj0efWMO9KXCd7a3KHjKnl+W/QYolVDahWF4DcJbostP+Cfo1zLBlyzQsG6FU4zO5j1IadYm7LYjocMFQ2GwlgnbNyQFbiRevFptaD83pWQhVbjhFAOGtCw64isjUXzBUyiwetF78vz9W9bL0216nXwmhZNBKVpISZBGH5fTidcemgRrcirAGJpUluD2F18L2PbiCQ9hZM9OaOux4pGZs9Bt+GXYsuwzHPQ0AQB0bd3spDOBJHFeY8NR8eU8VrFVVoAXLaRG5u8Kc/b5F8n31ePWhsvxTESPBhGk+/btQ1ZWFlq3bo0bb7wRR48KQmbz5s1wuVwYOtRreenQoQOys7Oxdu3aoNtzOByoqKjw+xA+hImyV4ipQ1gNCynv88xyVFWG3U2p9L0zht5X3oE7Hn8RHdV54BiPo8jCx4s+gkIUvTZX3aOLKwsK8MJDkzBz0sSIXAgixSn6vep8vGUryoQhHwNX/ze27O7nyAneC3SCRVRbUeYXRbtj9VLMm3YfXn9mEj77393Y+ssX6HN2F4AxOE0W/PD6i7W2u/Sl/8EmVmxJVAiCNE0R/rfAlfufCy0E0ccrawtxp2jNVbvrN21Q7+4d5XyIi6b5+8HZysvkabNYrzsYUrYAp9oDJyem8kJ0xFdWa+/w6d5de+TpIskFiTHojeFzxJ4KBw8ewcHHVmHr40JAjOSDKwVVEdHj7YcnoVqMpE8rL8Ldr7wZsv15o2/A2OdmQa3RwONyYuO6NWCcAs2Ri0HjHjnpfhg13muh2hPb6RfWffmp/HKakuj/u+4x/FpYnMJ922G0IG+3YFTYuvxLzJ92L96bdjdslWde/t1ZE+/HCw892CjcMcJR73et3r17Y+HChVi2bBnefvttHDp0CBdeeCEqKyuRl5cHjUYDi8Xit05GRgbyfNLY1GTGjBkwm83yp3nz8KUIzyg4yUIaZMhePM2emkFNPsYopzVwxLUvdulmxnhodAZo9Am47sl5GN6tKSysDNVcAhyJwgPYGaI0ZTBWfPgOnEYL7JZkvP/kY3VePxhurTAElJrsFQeSYNarG8bXTu0WhK5U+7yFSbBueNxufPvSJHy7YgPykI5SzoI97ib4du1O/HdwBzSihXnbIX/HfafViu35ghVbW1GGck0yOMaj18BLwvfF4f8z14nVmgJZ0aSUT5pTSPkUiAFj74HKJXwnR+3++503/QnhGmYMPYeOCrkd6ary6DjYxaA8TZQEaZdBV8kWm0qfW2m1XbRaBylUUZ+s++JPaDglUpgOVqsVkvZgJEijitNqRZl4L7QU52P8a3VzB/nm5cnIRQZUzI2+g8L/xkORluX1MXeqY7uE6MHd3vzKqVnZtZaf10v0a+c4fPXOW1gyawJ++PNf5CADx9EEqxbNPl1djQl2/L4M1uRUOI1mfP/2W9HuzilT73etkSNH4pprrkG3bt0wfPhw/PTTTygrK8MXX3xx0tucMmUKysvL5c+xY8fqsceNH2nIXqEILAKV4tg8q1mpyddCavWPxA6ES/Q5VNbIFdln9N24dfwjaKPwiij3SdROPnbcmxezwGBEzs7AATB1IW/3TvBiX3oOHCTPt4qizGRJDrjeqWLgvRZepd2Ga558Fkf+W4cP/nc/tlZZ4OLUyEQBepoq0BR5UDAPirlkOBOEAAaHOQkrPpgLwOuH5kowArwHFr0g7LK5PL9o72DoXf7D4Hqxnn0gK5pHTOul4+vfcpzuFqy5Lq0B237/HgCwY9XPKE8U0tskVJehZY/QpUqlK1xl0sChEKy4KhYd8ZWU2Rw6t/C7cRoS5fkO8WfGsYa3RpmrhOuY4zhs37oLBr1UGpYEaX2w6sO5fhb8SPnmpeeF+47Hg+sfiCxQTmLjdwuxyya4tnROrECnAZfWef++dLrwMnnalWDEpm9P/lnc0JTZvPed1mf3r7X8/KvvgsYp3FuLjKn4z5oCN6cSqqoBOFoQO65ip4O1y5bJ0wXV9WtEiAYNfteyWCxo164d9u/fj8zMTDidTpSVlfm1yc/PR2Zm8FyKWq0WJpPJ70P4wIXzIQ2c9olTKeSAJ3t1+CF7j0qqPlM7kbw5vSluevpdGFxioMdJJC62+ggLptbgs7ffrvM2arL+hyVihL1LroPscbtRLkbYZ7TsdMr7CESy3ntjNVkr4agqwzdff44TyISSedBFV4g7n3wNl09+BXdOm4c7brwa2cgBACjcLoDjsPHfnYIYfdjrh5ZWVYx8lTAM2L33gID7rome9z8XeiYIUlbDQuq0WuV5SYn1n67o5mkvATwPcBx+/ekXAMD3Py0XraM8brgjdLnPinLvNWpMM8KhEF6MVFHMXpfECUOEvFqDw/9sAOBTdSpMkv/6IMXj9Ss8uuMwzr/4Krme/eqPAweQEZHx6uTxWHWoALNeehlfzgxcICEYhyU/cKUSnyx6F58+dze2/PhJ2PXKC07g781b4eGUaIZcXPbgKyfTdT8y2nSCiYn9USjw26o/TnmbDYVTNB4oPG4073RuwDYtdOLvSqGAgrnRVV+MS4cNABhDLjKwbdW3p6m30afU7nWtsicYYyq7zcnQ4HfyqqoqHDhwAE2aNEGvXr2gVqvx+++/y8v37NmDo0ePom/fvg3dlbhFtpAGsYpIQ/Y8VyOoSSGWkgTgdoS3iHnExMoqT3D/Qj0T3tKkBPp1wSkOretE/87KpFSs/fzDOm/HlxMnBFcQld1rAT6x5x84OB3AGNr0HnxK2w9Ggl68sbocuPL22/DTvGdQxpmRwKox6rwOuPrxOVCqvaIvq113DL7qVmiYU/7ubJZkvPrwZFQnCwI0vaIYCWZBrDZDrl+pzlDoeP9zYZAEaY3r5Z+fvpbfajqe1+ckjjpMPwwGJFYLbgeVCcn49pVpsOsFi3BqVSGadgidZmr/7gOyW0rL9m3gVAsPpmjaAtPMYvotjsMXCz7Eyg/mwaMI/uJW35h570iEq9iOdn0HyK4CO7b+2+D7j1e2/PwVyo1CMC5TKrHDpsbLD90fsriDRPHRQ7D5+EJXcEbs9TTBdxv34Y1nHsDnz9+LXX/8GHDdpW8/i1LOggRWjRFj7qm3gMskTnTJYgx2SwqWzJhWL9utb1xaIXezb4R9TW58+mVwoo97WlUpRj/2Jjr2uxhNOcF3+781P52ezsYADp+Uc7xWhz8+fi9E69in3u/lDz/8MFavXo3Dhw/j77//xpVXXgmlUonrr78eZrMZt99+OyZPnoyVK1di8+bNuPXWW9G3b1/06VP/D8AzBtEyGmzIXraQ1szxqORkQeqMQJBKQTBqPvhbWKJaXKZQIv/AnqDtamKrrIBbL9yMzuvWAerKCkChwKq1myPeRiCq3YJV2LeG/ZH//gYAGFEFc2qTU9q+L/+tPIwfX98It9sNnTiEm64uhwdO7HcIQ3Dtk4CzRwUWki279UVbbZnwB88DnEKwjAJIKSnA0Gsvw1FeGEno1CXyVFU60VmYF4WKQRyyr2lS37lF/K4ZQ9eBl6EhuGj4EEEwKRTYWiZYSxVuF+547uWw6544LKZ9YQwtWmXBrRWORxE0nK/hyWzVUZ62Jqdh9dE82BOEERzVaRCkep/qz1qbWK5UrDxVBSqvGAqX04l3Jk8IWGHul1XrhN8Hz8sjFpXGVMx64X/48/MFIbf7zZuvA+K9MomV4rxUJ5ojF0rmQQmXhF2uDHz++0bMeeZ+LH7+Hnw54z58OeM+fPLcXTjIhET257RKQbMO9ZeOzqgTjQmiH/fO0qp6DRytDyoLCuTCK0aEjmnQVwgW3yqX95nWoolwLz/mSUZ5UeCyyPHE0X83wS0KUoVTMARt27o9ml06ZepdkB4/fhzXX3892rdvj2uvvRYpKSlYt24d0tIEP7FXX30Vl1xyCUaPHo3+/fsjMzMTS5Ysqe9unFEwLrQPqZz2SVFjyN5HkLqckQhS4YGn44IPRZoM3jKOO1b/HrRdTf78dKFQv9jjwQVjbkGvVs3kKkafT38y4u3UxCU68Rt8vprinMMAACNXvzfk4l8Oo3uuHWu/3IMEsxAN72AarPl2IZycBmmsCKPGPx9yGxffOx0mViFXSwKEoIgJb8zF+uVfgueUaIJ8nH/1PRH3Swr6cYhpkgzMK1R8H0qS/xbH8yedYiYc3YddAbVYyQoKhWCl1jigMxhCrwigqsCbUcBgMEAhuhVET44C3YZcDU6MbFdXVcjD5QCg4us3U0FNKqur5KwDAJDgFkSH1il8vw6NPuB6hMBHTz2KXFMKft91AMvnvSbP//LFJ+AQK6GlWwvxyCOPILGyGGAMvEqDlf/uDbJFgUK3VyRlJwIX3/8Cbp/2Du665Ub0NFWgCfLBMR6FXCp2uzKxw5GOHY507PMIYvQsZQ4G3fJ4vR5rRot2woRaAfAeuA2JeH9qZJXhThfrvlnsjbA3hR5hM6mFdg6d974xcNwjMLJK2DkdVi8K/4Lb2Fn95edyMKgk5KtiPGgtHPUuSBcvXoycnBw4HA4cP34cixcvRps23nyGOp0Oc+bMQUlJCaqrq7FkyZKQ/qNEBIg/YmWQ3IMKBPYhhZKTk+m7bBEEsYhDvAm64MNIJnF4GQByDx0Jv02RA3v2AQDUdis0BgNG3D8ZBikvZ7VTKGl5ErjFJMtpqd78l9ViRgG9on59/JLE77IspxJJGUImiHIYcYgX3tx7dO7oN0wfiMSkVHTMFB6GHM8jqbwA7Tum4P1pd+OQR/idtPepox4JUiL8ak4QSDrJcsZxOLbdW+zAqRRuZipPw/ohNVN7t69xWHHj0xHWpq4Wzpf0uDdmeH3JrVGy9iQmpSIFZQCApAQn2nAF0NsFX9cmpoa1kP7263K/zBoG0VfYKJ5nj4ospKEodAr3Q6ZS4+/jhVjzkVBYYVcFBMu9y4n7XnoberMFD7/8Jlp6BHcTj0aL9x+fFHCbBzb8CYdJiK5X8i70v36ivCyjVQdcPvkV3D3tbYwbPRJd9cVoyZ1AS+TIn/aqXFw1qXbKt1Ol95V3Ip0Vws1poBYt6AV6U70EjtYXh/cdkKfTm7YM2bZXv34AALc+QT4Gjc6AZlrhPnC8rPEH+ISjoFy0InOc/Gx2JRjldFiNEQrFjAckQaoMfDolCymr4UOqUCm9FlJ3aBGSs3ObvJ+0JulB26Vnt5WnyyvDV3+SqJSS1zu8N5JLr7oMnNsFjz4B782oe5WS49v/kSPsew29SJ5v8wjHkaCrv7dJt9sNs/g9u61upIlDuTynBDgOLZCDfteOj2hbw+58BpkoAFMoUGpOx/oCFY6hCXhOiWzk4MIbHqxT36Sgn3Ix56qaU8qq7thO7xCPZE2WSmI2FOOeew0qpx3gPRjYq3PE66nES1RyPWneRhD9HMdhz8799d7PSGliEPpTwKXiADJg0wl+sbp6vL4CYdvtn4NWsnz36CP64ysUcjYDojaOBO8LDVOpsXLvYbz64H2CtYkxdDT6t7/lf29AKaZzO6E0BCxf+fMnn8nioKWyIGhxiZbd+mL0Y2/ilmfexS3T5suf66e+A70xSPGIU0CjM2DwsEuhZQ64NHpwbjeYWoMPFn6EFyY/KH4m4eUJ9+G3+aHzpTYU5Q7vM6jNOaF9+8+9agwUTgfAcVjx2SJ5fp9LxkHBPCjgUrFhaW1XjHjCLsZcyPAeQKHAzwsbrx8pCdI4wJv2KZyF1F+QCkP2Yo5SV+jhxX0b/5anm3XoHrRd867e4DQbH3keRqdYmz3B54rsOHAYmruEN97qlHQsquMQ08YfvwMAcC4n2vb1RqRXM2Eo05LWtE7bC0XRsUqopWwHTg8yWnWEQhrKZS5ceMkNEW9LqVLh3HP6yOsbWRXOUubg4p7ZGDd1bp0DHVTi9VGuqm0Fzz/urb7lTfnU8JGaU1+YiWnTn8P519wS8Toat3itioK0bbuWYKJf7ImDOfXex0gZ/dhbuGHkBeisLUAq84qU5KyWDbrfpGp/Nwed6E/aZ/RNcmDTuuW/NGgfGivrv/xYzhGs4YUXMKZSo9wsvGxr7VW45vHaL8FttYL1m1dr8O4zU2stL9N4RULncwbWd7dPiQ79RqBrimgVFo0XbkMCnCaz+LGgMiUdf+YU47nHHsVLD47Hv8uXnrb+SUGtCo8bzTqGL7kr5WzOL/MaPlp064NmUnDTP+vx45uPy5+dq+Pn5aw05zhcPqnmAMgvQoXWxhtpT4I0HpB81lRBBCmTKjXVtpBKaZ/crtDDi8cPeatAZHc+J2i75KwWUHiEbbkijLR3OZ2yc3Z2djO/Zbe9+CoMJUUAgINMhV2rfo1omwBwIke4MflG2Dtt1SiHYPpo1qH+6owXHPJWCFF6GJRqDRIg7LeNphht6/hw6nXJzRjZqw0u6piKSVNfwI1Pzcd5l992UlG3khXcpuN95glUVnpTKUkpnywJsemHpPEIxy4JUoOP32lVYXQrtLTrPRTXTJmL+599A1f064wh7ZMw4Ma6WbLrSopbeCBJoty3hKpSjEIud0fTwzZ22fin9wX70sF9kO3O9RYyYAzDBwbO+jLizsfkdF6l5hTsX7taXrb+y4/hFvMIW9wlQYMXo8klE19ES+QAnAIajw2pZQVILy9CenkRkksKhNLPjMGjN6DKnIZv12xEWd7pedlzixH2Bk/4Ii0AkCjm+a1pKWzTtj0A4DiaYGOxTv4sXfEX9q7/rR57HD2Wf/CO7IMPQDBeiDrAnth40z+RII0DpKCmYGJFEiSsRlCTUq3ypn1yhxak5ZJwYQzm9NCWRSkK3x3GX1Jiy9IvBGsF43HBmJtqLR/3wAQo7VYwtQbffPdTxD+2Kk74PnQ+AVv7NvwGnlNCxVxoU48WjLIcr7DTiF9z+xQl2ipOYOTdz5zUNs+97Bb0u+7+sH6noXA6nXLYi9Ksl8WLVCzBLr6I/LvsO/mG1uPCQbW2EwvoxWwBLp+gOukVi1U1fER7pPS46BpceH3dkqGfDCYxfZcdUj5Wr/jUiIFNdnX4YLEzkUq1lO/ShU4XXILb/vcOuia4oXbY0MReiLNHXh1wvaTM5mihEKsKKpVY8tkXOL79H8x7cAKWb/hHbtc2TRdw/Vjg0jseg4lVwKnUo8ScghKTBSUmC8qSUgSrm49fMlOq8M6smQ3ep9Kc43K6u0iDTTt2EXJIuwyJfjEGF4x5AO2UOchEgfwxsio4OB1+/2kJDm5Zj/898hBmTYzMhSoWOZEvFgDgOKiYGy0U3nLFvEaL1QvfiV7nTgESpPGAbCENIkhZ4CF7pY8PqccTOsDHIQ6/cxEk+9aKQ2CeCC2k2zdtEvpjtyEpq1mt5Rlt2qNX0wyA5+E0WTDv4Ulht+lyOuFIECxITdMs8vycPUJuRjMqT0no1aSq1Ot3KcU2XzLxRYx9+t16TS1VV3ILTsiBL8lNU+VaXVJ1I5d4/rf+tUJYwBg6DwxdvjNaaHnh+nb5VByT/EkDeCPEPVLKpwKF8AD3TX+VIP4GI30pPJNwOZ1w6IV7g8lVLr/Ij370eTw540Xc/eLckOt37+v1R7cmpeK9L75BnjlFto6qPE4MvDm2Ith9SWnWBn26toOKucBzSrg5FdycCjynRAKrRltFDoZ1yYS5uhAAYDMmYdFTjzRon9Yv+UJ+jqWaIhuh6T/uTiEfqUKB3xe+K89XqlS44an5uGfaXPkzYvCF0DAn8rl0fPfZR3AnGGFNSsX25YHzwcY6Np/CM6lcCZpkCkYiqTrcjm27otKvU6VhCnkTp5VwFlJOtpD6z1drNfIyPowgddeh+oyec6ICQh13l9MJdZiqTWVWJ6AHNPbgwTQXT3gI+ybeh9LkdJQmhK/UteLdOWBqDcB7MPyu++T55SX5ANKQwNVv4I6z2uuDa4xqIiJ/9uzdh06iRG7TtiX4dXuhBAc1U8IGDzxiSqiyaidgEl446isZd30jpa9ywnsNSoJU447NPkdCaW4VyvKr0apHRsTrHDh+AFrxd5+js6OFzQQOQjUrk9mI1q1boKjQDnAKnNi9LWzRgTOJVQvmAWIKu+bJdS/g0WP4tVi39j7kIV2McBYqwTGlCuA4tNYUITEptb67Xa+cf/U9OOu8/SjNPSzPU3AKZHfrA42YSqnjBcfwxpx3wJQqHIQWe/9aiXb9hNETa2kJfl/wjl/+al1CAi66Yzw0EaRwq8mRg4cAk5AqLz07cCBYTdQaDTTVVXCYk3A8tzBk284DLseBLX9gS7kGZQbx3HAcVv3wI7pcFJsv4MGwVVb4lSo2qzw49/LbsP7td+SiHFUnUbo7FiALaRyhDnIReofs/S2karUmYgupO4IqTRJmnSjIOAWObF4btr1DtOLoWeg+9B82FADAa7QBI1x92SOlkaqu8rO6WsVITr2qfkUjs3n7bmJcWBeI00Xx4SJ5OqN1iux/KaWCcqh1eHHSBJSKVWkk38NYRM6nynm/W7d4PFo+sP90Y2Db65vg+WwPju0I/VD1Zf0ff8rTWecKmS04jsOWjcIIwOBbJ8o5UZd/0nijbhuCHTt2y9O9Rlx7UtvITrUAEIb8ja4yMJUa4Dg0Ry5G3H7yeZNPJ2nZbdGu91D50/a8wbIYBQT3hM4JQsAQU6rw1ddL8fWMaZg1cTxmv/QyNlc4sM0B+bOxpBpzH3v4pPpS6fbeP9udd1GIlv4YxOeRNYKRgFETZqIFcvxcEipUjU+4/f7+XKHwguh+ldG0FZIymyODK5bbuBKMMVuNKxQkSOMBMbpOpQn8ti8HNdU42yqNWraQejx8zdX88CilcojhBUtSSrI8vXvDurDt3TrBgpeRlhyyXefBI+Ra6Nt+C10erlp0F0iokfDfKvohJiSGt7LWBc7nhqrlOJTmxUYVFE+ZcL4YY9AlaGT/S62Yi9SlT4DNkiJcQ4whnY88VdfpRs0CCFIxt66Wr7ulKxYoK6hCayih4zjs3xh5dRnuoJiTlTGcN7iz7BtcsE/Yhs5gkIMLS6yx8XIUK1SLokvlcqBlt5MrWT1o3CMwMCt4pRqVagu0zIGzLdW4ZeocJGe1qM/uRpUrH34JRpcQMOg0WbDNIVQkY2o14HFD4bBD4XICokGj3JQU1lgQCIcY0KT0uJDRplPE67VqIRgbnAmJsFVWhGyrVKlwzgWXeIPXADgTjDi0KbzRJJY4eFDMjMJx4BiPHsOvBwCk6MVRTLcTUCjwn9WNH15reP/f+oQEaRwgxqdArQ38lij7ltXwIdXpdPIyPkyKJqlsqCaCcohJmdnydGFufsi2u9f8JucK7XvZFSHbagwGKMTSd4d2BU/+ay0tgUP052rVIstvWSUTks6nBskPeLKoa+j53H11vyk3BGqreH7Fv53iVBLvX4nJ4KzEqJ5tcNeLc05n9+qElE/VrvReg9LwvW+EeWNiz1pvBHNVceQvMSlW4YXKwXmg1qnk88uXegP+1GI5QQps8mKrrIBLFKQWT9lJb0dvTEIrnfDylokCjB4xCJdNmh2z7i4ni1KlQt+e7b0zeB5qWxWSqgvQWpmPRI1TyNsqukAwtRqfzZhep328OuFeuMT7dWIdX4iH3naPIIaVKqxYED6Q55+Vf3gtpGIJ418+/bRO+4w21UrvNZaKEvkFqN25Qu5WplRCVV0JKFXYXFSJ5e+8EZV+ngwkSOMB8Qem0QWO7JSH7Gs8s7VanZw0P5yFVCobqlWEbgcAWT7plCqtthAtgU3LhTROCocd2d2Dp5OSUIoR9iWlwd+Gf5n/JqBUgfO4Mexub6WUwqP7Uc0JQqxNr4Fh91UXdDX0fFlubFgatU7BcigN1TsgiLlMCP5aYDw6qHIxYcrTOPeKm6PSx0iRrmNHAEGqYY1TCBQdLJOn3XXIFJDsEXzIKiFWZRLPr87u/ZHrPYLAdTXycoL1ya/zXpfL8rZuGnpEJhxXPfIGbhh+Pu588jW06zusProXk5x/9T1oiRPCHwoFXPpElCak4yCaooIzQc2cwlC4GFCTV4fQlNcn3ofyFK/vdJo29POiJoakZDkf6YH9h8K0BgoqhJRSSpdDfm6W1hw6BFB4aL9fWeWGJlKr8vHt/8DhM7pnUXpfQDv1vwxmVgHGKdEpnYPKWiVUITuaJ1chi3VIkMYFwg9LrQ1cu1p6kHM1at3rdQZwUtJ8FkZoSmVDDeF9dZq27ynfnBxc6EusqExIl6S2RfbjV4kJ/O3u4P09fFywymqqKqE3en+8B7esAgAYmBUZrTpEtL9IqfnNV5fERuk6nUcQpG5I50MQPUZmRHtVLoZ1zcKYqe9AbzRHrY+RItVtd6u8594pHo/kX9rYcJZ5XUo4Z+SCVEr5VKIQ1neJwjzB4/19NkmxABDyy5YX5p1qV+OC/cdFtwjG0OuS2inm6oJSpUK7vsPqNVtHrDJ87ES0VeSgFef9tOZO4NwUO+4fPx63TpuPJi7hvusyJOKPj98Pu823HhCCVAFAKVYKTExICLVKQHTiSEC1IrwQdojuYYk2b95ih9GE/AN75L8/n/4k5iz4EC8/8XiD5/Nc/+XHeP7hyZj1yqv45sVnw7b/av58wX+UF+6BKeleMa9UqZCqFMR5lbUcY668FEqbkC5x1a4DYV0aYoHGaVYg/JEspPrAFlI5yl7pL0gNOoM3qIkFD/IpPnpI3keTZs1DduXDl+YivSwZKuaCW62FO0w9bbsYLKWLIFgKADTMAxsAV5CqVABglao+1dhm4dG9AAwwIjLr5aeLFyD9PxNyO5bhpptuD9nWyDiAAwrBIw0KOKpiIzFxzdyddoUb8ABapsL1UxtXrjrpCuX13pccB+cGGKBupO/WaqcHEMW0NnwCCwBCblmduE65WnrR8CABaiTw3t/biDsfwK633gY4Dr++9waumVL38rsNzbyHJqDCxePaW8ehZc/zGnx/NjHdk8Zlq/eX0nimSduuGPv0/JBthlx1PT7+fiXAcVi7eSsuHOtdNu+R+1CgMUPh8YATAz5dljRhIePhUWmgY3Z0H3xVnfvWNC0ZFbzgRxoqq0vx0UNw6QXB27VTR2w+dhQ2LgFQKPHD/Ldx+4uvYf/a1dhj5wGNGg5LMuY9PAkT3gidBiwc378yA9uP5iKBd2PwpaPQ5aJRcDmdeO+xh5CfaAFEi+eu/FJcGWI7hYf2o1wqKyta+bsM9P++Mptk48AJNwp5M1qd0xdXVlXh69VrwWt1+O7VF3Hd08+f0rE0NI3zLk7IOK1WWSzqEo0B20jD8lwNDac36L0+pCH2sXPN7/L0Wb0HhGgJ9C3ohK7upjAzQRS6w6R8colvrCmmxJDtJPRq4R3KrQ4cxFJ89BBcYv7RTl06+i0rKysDACQowluibDYbum1pgQ58OgZsPwtLnvkQxeWBh1Vcdrec6qlAHDr1jbqPJjox2McBH0EKrz9mY0ISpCqz95qS/ElVAYbdGgMmH1ePSD091/7zFxTib16dKgw7W8Whez3zfjfmtEw5b/CJwtJT72w9s3j6E8hLTIY1OQ2ffPo5cnZua9D9leYcl6sBJbPoVvaKR9qeMxAmu3CPtBqTBEMGgLkP34s8Qxp4tRZunQGuRBNciSavLyenQBPk4+pLR6BVzwvrvN+ht9wBeDxgag0WPBE8yv+3D98HFApwLicG3Hw3stVei2Gh3QOX04kvv1gixDSIv5tiSyp+mfOq33YqCwqwcMpDWP/lx2H75nI6sTW/BA5LMkqS0/HVnxvw/EMPYvbjjyLfnCL73gKA02jGpm+/CLqtxa+9AqbWCLlXASSxMjTr0NOvzXlX3g4Nc6KSS8SSlx9El4tGQVspXOtHisrC9jfaNM67OCFTXeYVSRpD4OEOeche6a9IzQkW2XqKEBbSI/vF4QzG0DxEHfvK6iroxCH6RLFePB8iOf7m776ERyz7dvbAgUHb+ZKSIrwheoII3V/en++96dxyt9+yIo/wyE9LSwu7n8/f+QAWhRo8Y1BwHM5ztMbRFzbjmx++rNW24Eg5VOLN1aYW/le4Y0OQasVBEIdoIXWoalf1aQwUlZT5JPj3nj+HQjgeZSO8lbndbqT7CGlzhOfk0H975enm7YWE2FaFYJHX1XBdUItBgDZVbAU2HdjwJ/bamSxKXAlGLHh/AQoP7W+Q/W1f/iPefukleX9t2maHWYM4GQYNvkAOFvrk1dmY/+h9KEgQ8rUq3C5oXf6uWQrmRld9Ee548vU6l1eWSMluBVOZkPIoT5MQ9BrKKRCelRprFdQaDc4ddp3sWmY3mTHv4UlwmJPE/iuFlzmFAhuO5srbXPHBXLz+0iwc1hrx8479eP6Rh/DGxPuCJtj/dNqT8IhWWSkNm8tohtNk8Yv2ByDkRf19ZcDtVBYUoDRBcKvSO4URviRFbTc3c2oTdDIK83fZkrH+2/eQrhOeAfZEU8yXFG18d3HCD0eF9y1Ppw9sZZQEqaJG7k2tTx5SFuJhWFEl1hZmLKS/1L87/5EtNyYIwxBMoQzqu7Ji+SqA46Cuqog4OXHrrkKCb14dOBdpTonwNqitrvQbutn1x48o4yxQMA96jRoXch9OpxM9ctsBADbrDuH3tG1wMoY0Toeef2Tg6+8W+7UvOFwGAKhkDEwr/PhrRt1HCynfqI0T3qrdWrH2cSMTpHv+8/p4tevkzZDgUkuCtHEdDwAc31mKRJ+ciBbGwWEPb73X5QkveYwxtD1XyCJRLQY3aGpELupcQpCIQ5+I2Q9PxLJ5L9dL308Fl9OJLz77ArxGC87jhs5tBXgPXEYT3n3zLVTk16+/6wePPYivV/8tiAAAHM/jvMtvq9d9EAI9h18HrUN4XpQYU5GjTxPEqMuJu2+5CVOen4Ur+nVGB1UuWiAHl5/fBaMfe+uU/XBvnPQAFE4HeI0Wi14JfI3bxGwuCWI6tLbnDUYWxCwwShWKk8QXXU7K260EGINHb8CCV1/FmxPvw5rDuXAbEgUxyRhcCUbZ8vnWA/fV2ucxt4/o5GrcozgOyawUF3VMlUcyqs3JqCwoqLWdRTOmC78XlwuiRxqSLZaAx3nppJfQHLngOSXW/rMDA64dDXg84DVa/PTGrIDrxAokSBs5dpvXH1JrCpxbU7KCcir/h5VGowEnW0aDP9AdorhShElcf/zwYXk6RYri5jjsXPlLrba71/yG6iRhuDFbF3lASteho0LmIrWLKV1MNVJc7Vwn9CGdK0Z6i3Yh9/HJ++8jldPBzRiajm6PcQ/dh4OX2lHMHFBxHPh//N9My3KFG3Alx6A2CIK0ZtR9tFCLFjNpqF5pEm78jU2+5R8VHhyMMTRv0VSez4tFGBrb8QDA4a1CgE0heHgYg4rjcGJ3+OT4aTYLAMDJ8UhMEp5ONo3wwlHTFaN101TZMlOdmIx1uRV49qmpWDRtcj0eSd1499EH4TAnA4yBKVWwqwxI9FQDvAdOkwVzZr5QL5Yca2kJZk28H0f1ZiFxvWiRaq7Ii2o533ina1MxQFKhkC2jt1x/NTLaCj67PS66BmOmvoNbp81H9+Fj6mWfGW3aI0MUwhVJqdj8nf9Ilm91o9ZtvHliWzf3sZT7CMb2qlyYWIU8z5qchuLkdECKXRArdAGQf19FllS/vJ+fT39SEK8AUtxFshC3sHJwjMdZihzc9sCT6Hfd/WjGCS9hTKXG57P9/TxtlRUo1gpWVoOjEmWKJKiYC10HBfa3VapUGHnDfUhkVSjjzPjzh4+grRIMNfuO5ARcJ1YgQdrIcVqr5ekES+A0JtJJVqhqCz/pJxjKQuriBJGlCFPNye6T6sjEvNbaIzt21Gr745dLAIUSKms1xvg4Wh/f/Q/yDwTPMSrkIhUeVjVzkR7+ZwPcotvCORec77espFpYJzmCF/Euh1sBALZrjuO8bn0AAEMvGIa9BiEhcYrD4tfeWi4Mi1aDwWARBIIhRiSSSk6VJJw7Y4bg8sBxHIpKi4KuF2s4SgNnYVCbBatHbHzbdaM8R/jtFsONCjHBf97+srDrJfPCb6sC3qA93iQmxa7R9orJ0zCkYwsYK4vAedwAx4EpVTjAR+azXd+s+GAuCkzCfYoTBSLHeFSpTUi2FwM8D4c5GV8899Qp7+vtp6fCmiwIcunY01gRLr0tduvMxwOXTHgKSrEgCedxY8yoYcjucnaYtU6d22a8BHV1JaBQYvmKNX7Lfnt3juCv6XFjyG33yvMHjXsEWo9/qqmO6nxcP/UdjBw2FInMJwBWvF5VzIWu+iJ00hQghZX4+cL+k1cqR+zvr/But1v7lrIQn/DUbEy8907c+PR8JIpZBlo0bym3za9Rde7j6U/Bo9MDHg+s4rB9F2M1WojPpkBkteuOc9pkAIzhEMuCQRwhsyUEjjOJFUiQNnLsVULaJDAWtIawZCFVKIML0lCPdI8YCa/0hB5O1Jd7o/wTmDf3YWGx/9D64X82oFIUz5nMBbVGA6fdii9euBcffLYEn370LqpCiCXpZlczF+mqzxcLb+QOO84bfYM8v7woF/lMsNi27d4v5DF8tOBdNOES4GEMhhHpfsuqzcINJtnj/6N2iWLXDsCUIZwDY4xYSKWheada6FC7zm3lZft3N4y/XoNQLQjqmp4QfgK7pOz09ukUYWImhhZMCbPoS1qeVx1qFQC+KZ+8VkRz8yR5uub3cOGY2/DQy2/hmef+hx5GyD5+X8544hSPoG588vTj+HP/McE/z+MBUyjQFHk4J1U4jlJ9KnSiJed4xanlgPzy+adRmSwMwao8TjClCimsBFfddDfSstuGWZs4VYb0bA9TZSGuHtIP7fqGDoStL9QaDbqI9wO7JcWvdObBw8cAAJrqar9UgEq1Bm31lfLf7ZQ5uO7JtwEAHftdjOEX9oGeidei+EJz+YBeGP3YW7j2ibmY8OwbuKxPexh44Xfr0enx4etvYulL/xMCtwBY3CW44PpJ3n2qVEjK9M9W0//GSdDwQvoql9GElR/MAwB8+MQjyNEIRhYl84BxSjRHLi6d9FLY72PgzY+gnUqwvFYbzUI2A50ev81/M+y60YIEaSPHaRPzXdZ0kPZBSj+q1tTO8hXJkL1UNlQVpmyoyeEVxDp4a+1anf7rffPBQkCpgtJuw83PvoBdf/yI92c8gp3ODPCcEuWcGasWzQ66H5U7cC7S4mrhu9Ba/R/qG7/9AG5OjURWhe7Drwt5DG33CD55u1V5GNxvqN8yY2tB1JqhhdNnSJGJPg0uBZDR0iK04RSwV0ffgVz6gXOJwktFdtssuczkif2Rl6qMNmqnlA3C/zo/q4vXn3Tfjn2ntU+nik70L9NzStn32lHuCLUKistLoBF/q+U+PuHd+grRthzHYevf/wRd/4qHpkEhpkM7VBxe/NYHhYf2Y/aE8din0Ml+o0yphIFZMeyqWzBqwky05E6AcQrwCaILgsnilxuyLhzatBa7ql1CaUWPG26VFkmsDJdfexOatO1an4dGBOH8a27B5JfnoPPAkad1v5c/PBX6UiHAaXuFHW89cB/2/rUSVtGoonfV/n0NuHESmiIPHdR5uG6Kf4qnrkOvxkV9uqMJ8tFRnY87H38RXWukpjp7xPXofVaG93mXnIp/i72W1c7ZqWEreGn0CchWeQ03G7dux6yJ9+OQJkGMrHfDo9IgkVXh4rH3R1wR7MpJs5DGiuDkdPII57ZtuyJaNxqQIG3kuBzSsEBwQSpZSFUBUiUZRGtLtSFwkBDgLRuqZqEFqdHjFaRKjoNKTN7rVHr3m39gDyrMwltsqrMay+Y/g29++wv5XBo0zCk7mR8rrkQwNKIDeM1cpA4xiMkAf9eC3JwjAIB0RXnIH/Knn3+IbJjAGINrQO2x/YEDhoFnDGqOw6r13mhIhUvYn0elQHqbJHjEG1POvuKg+zodFJeXyJHpCakWAIDK5/gdxXWrihJNtB6h3zUFaevWLWSBnX+kcSV/Tw7wEsjsod1ifv/tJ/mcqpO8VtGmTdLl76HoYGg/VCkpuE0f2Oe8Pvn8+SmY+94HqE4RrJUaWxWYeD85u7lZHna89PbHYWblcCr1YilIJZa+Xfcyti6nE59//JmQusfjAVOqYGIVGHXZlcjufG79HVgjYMEbc/DRgsZRoac+GT5iKDi3C7xWh6KkdHz660o4xMIfzTJTa7VPb9EOd06bhzFPzgv4fDh75I24e9rbuO7Jt6HRB85kM+Cmh9FO5X3B58XsMYnuCgy+LbKRiLZdvNenNTlVcDcBoKssBVMJBp5z22bW6aVKbzRj4OCLoGZOOeNNdZBjiAVIkMYA+Qf2oDTn+Emt67RHYCEV/1cFiGQ8y90MOqaGS6vBe89MDbg+Lybh1Ya5WnyH6QFA7xEr6/gI4c9ffx1MpYbCaUerdmnYUpYAJ6dBBivAVcP6Y/ClY8AxHvlcOrb8/EmtfRw5fgg60RfWd7sup1NOepzdzD9gocgjzE9LzwzZ/7T/hHb7lcW4ZNgVtZanmJNRyQS3hRPbD8jzpYh6TqOEVqdChSiaCo8EF9Wng117vL67Ldu2lKcluzJnjZFUABEgCVJXiIy59tNk8asPrFV2pAdItaYOUYEMAOwHvG4qqS2S/JbJ57Ui9Itjr55dxYAiJX5++8XIOnwS5O/fjV0ONZjPfccpZgJpq8jB0DuelOenNGuDvt06CIGTomtREV/3x9N7jz0Eu0UImIJSCQXzoF/PTmjb6/QMG8cK3y1bgotyumHg7vZY+FrdhX1jpsfIy3FZn16wFOdDZasWqgwqFADvwdBb72qw/V45aTZSeH8jRKdMXcTWzHMvuxVmVib/zbmcSLIVwJ5oAQC0U+ViwE3B86wGo/OAy9HFLFqGGYPbkBBRDtVoQII0yvzw+ouYt+AjvP72O5g5aQI+fOKhgGkfguFyCMPCXAifRemxp9bWFqQqqNHL3RoAUGpKxv61qwNsQLhMjAmBS5NK6GvUE08Qk3R7xGpNi6Y+glKT8BA1V1VgmxgE1Yo7gdunvIQO/Uagba8BaMYJVtIdG1f5bW/V2hXg3zyGbLVQ0cU3F+mmbz4TIml5Hv1vvFWev23VtyjnzFAwD84Jke6prLIMrd2CFedIq/yg7UrE0my6Eu+xSp6z6gRBIFeJJ6Oy4PTVQg5E3kGvxTC7nVeMS1ZGrbPxlNuU8qm6udqCTb70rbGR+zUS9q/LlXPX+hIuO0NahTdwsXUv/xcvqTys3hH6AThg7D1ympkd+0/uRTgSFs2bKwgBxtDUnYs23Am04U6ggzoPV0yoXTWqz+i70ULh/e05jSbsXvNbxPvb9O0XQuUbeAOmOupL0PuKO07tQBohZf8J36OC4zA4tysWzDm1akONjZ6XXIVJb76NqS/OxjnJqejuyMY5tvZIymrWYPvUG80YPGQoVEwsg+qpwNA7Aht5AqFUqZClEQxMapcNJqUVpXohh2sr7gSunBTcjS0cl0x8EdnIkQOw1v7x10lvqyEhQRpFfpv/JjYXVghCSqmE3ZKCQxojXnnjdbw84b6IrKZuyT8zRC166SRrAySTZ2Bo78mCxuECU6mw5DP/ShGlJ47LF3Gz1m1qre+LtkZSbiMEiyOvUmPWxPE4oEoAU6mhtFVDZ/GgmktAEivDFfdOg0bnHe5v1UKIcj/mSUOJONwOAIfX7oaa45DNCeKKV2tkN4PtG7cAAFR2m99NZ+96ocpUOlccMpjh28WLoeUUcDAeV4ypnYrE7Xbju1lrkShWoEq2WuRlUkS93iK6P4jzbeXRrWfvyBeG5BljMCZ7v19JuOjcjadysJYX+upEbdEpCWyVs/HcznL2et1jbLz3t2sMky8gyyX4MVvhRkYLs98yyXrsW88+GAabYGmtbqBh+z8Wf4CqBOHl01RVhDv/9w5ueuZd3PTMuxjz5DwkJtUeOgWADt3F8qGMAZwCy79eEvE+f1v1B6D0BkxlIR9XPfRq+BXjkIxy4TpxiYU9Bh/tgoXvzotyr+qPX59cgnWP/wSrNfxLvyV9MM5lZ6GHsgXyjzRsha7OAy5HrwwORlaJ7s0tfs+1SOjW/1IAgEutR7nCAj2zoU+mB+OeeRd6oznM2sFRqlQYNW4ylB5BLFcYk8KsER0azx08zvjj4/fx15FcQaDZrUirzoOuqkxw+FepUZmSjrdefx3L3nol5HZcztBBEDabTfY502pr17rnwaCAAini8Jg1KRXL570mL9+6/Dt5utuQS0Puq2ZycjPz1t21ihGvhpJCnGWyIlfRBErmwfnn9IQ5vanfegPGThb8yTgN1nzqfaCYywWBa+F0ggDnFNi+YhkAoNwhCHON3d8vssQmzE/Rhn7Qpx+xAAAOqgphMVr8lh3ZXojfpv6Js0vcSINYetDtfZBLEfWmdOHm4xB35baFT3LekCisYhnTGvNdopVRKit6Muw8sAML3p7rF9zVkGjEl51AgtQjHqHGfXpuZ1s2bMUX0z9FRfnJu2TYirzX6T+J3mwHoR4TTqcTFggvPcdR+3dv54TrzcCHF6QdsjMAAEypwh+LP4iky3VizdZdwoss78Htj0eewqn3FXcgjRXJL8HlmtCjMhJLZkyD3ZIiuyLomQ0XXXnTKSdcb6w0cQuCdHXGNhxBOZQch4H7O2HRh+9FuWenzqZNW9HRnYpmMGLpvKVh25f7pCPct/ZEQ3YNADDyvufx0LMv46K7nq7zuh0vHCXHUTRFHsaMvhIj7nmuXvqV0aoD2uuFewSvUuPXd07e4tpQ1PsdfMaMGTj33HNhNBqRnp6OK664Anv2+EdLDhw4EBzH+X3uueee+u5KzLLh60+xcud+MLUGCqcDHp0BhQmZsCdaZId/oUJEAtYVlOH1CfcJNesD4HELFxgXxIfU6vQ++PQB3tbk4dvWWdCWlwIch437jsiJqY8fOiD3Jzmrea31JfYd3idHCkvbTIJFXs65nGjjtmLoxRdgv1N47HYylOLcS2sPoyvVGjTTC/s/VsnLx5gqrseBg0oMJDok5jh1iGI7QeH9HsoLTiCPCZaYNj0vCNp3m82Gtk7B6nq8ib+7xLI5m1C5aCe61LD+JonC1F7tlK1aaS2E43WJaQ04Z3SHkLXi0K2nhiSVRJ2OnbyFtOzdw7joSFd89sbpecDJglRRW+RLFl/JitrQcEsKcL61ObbPDODeEiEasaAFYwxZI9vBIY5wGKBA4dHAVpzlq5dBKf7GynS1Xy5tUj17hH/RuHTik0K9bo7Dxo2bT+oYgvHxsw/DpRXuNU2dpTCnhfbdrkkzMbcsGIM7wYg/Pn4/ZHun1YqdpaLoEL+fHll6tOp+foi1GidOp7PWpya7D+1GkngNtOrXCS0m98BxVELFcTh7R+vT3eV658CybbKRxVIS/lp3Vni/o5Igv61Y4opbH8TIHs1w29S3QuYaPRmufWIWjJXFaM0qMOjWB+p12/VBvQvS1atXY/z48Vi3bh2WL18Ol8uFYcOGobraP+DgzjvvRG5urvyZNSu2S1rVF7bKCvyycatYBswJXgzMSWeFaIo8NEUeTKxSrnABhQKlKel4+cnHA1YvcYtiLVhQk83qfTs0BBKkor8j8zBc0KsbwPNwGs1473GhkktllTXk9iW2b98qT5dA6KeJCfvjeA96Zxuh1Jbh97Vb4OZUaIo8XPHQa0G31++qu6BkbhRzyVi35B0AQLLHm8xbKx52SWkFnFYrXHphXy1bt5Tb/P3lXHg4lTB8MvSaoPv6eslnMCiUcDGGi669TJ6/9IW/0eWYDRZOgaPwYKNPzJaOU2DLjk3IO1AOBceBZwxbv1iGHY+uAK8QrnWlJ7rJSHVu4dpy1wgEcoiCVMtOzkL6yScfIFssDZtdcnoq3qjEPJ121BakssU3wuP5dPEC7H10FT57fn6d+2G1WpEium005034bkl4C00gmoklPsvhwgXnXogyH4vn8V2Bc/AWbDsmT6e2qz3kXS3Vs4/wRcNgEyy8VbqTHwqsid1qxQGXFuA4KJ123DnzrTpvY+DND0PH7LK43Lg+tGB+f+qj3nKOANopczH87ml13u/p5ptnPsSuR1fitz9/jaj9n49/j4Kn1/t9tj+1CsXl/tlR1v26Epx4T2LfcCj8xwHdOCGdnVmhxrqta+v9WE4nTau94wgZ7vCJ3pUO7z2Dr4x+Kr5wpLdoh95X3BFxMFRdeejlN3Hz9Ff9SmvHCvV+xMuWLfP7e+HChUhPT8fmzZvRv39/eb7BYEBmZmRvzg6HAw6H94Zd4VO/vbGxfP5b8OgMgnVCpQQ4BVpxORg7da58AW5b9S2+W7kBLpUGxqpiVBqS4DAn4+2HJ2HiG/7O6YL1UB3UQlplt0KScXpD7eEvOY0OD1w49nZsmHAfKlPSkW9MwQePPQiHGAms4ENb+yqOlQMQzmeBwoZUXgsDhAueKZRYl+sAkAVwgJFVYtjoW0L+4LLadUdzbg4OIwv7dv6LFgXHYeJ8AomYBtVgsLk9+HPxQkCpAngeg266HQBwfNcWbC9wApwaTVSVIfdl3CMImSOKEgxMF65Rt9uNZhUuAApsVngw5JE+OPxfAfDzETDGwHEctm/8B53SzMiEUDa0u6MFVAoOJk85ACM0UQ5i14tDtzUDgRwKN8B7rY51JXubVww1dyfD6XRCU+Pm9vnzn6B5lQWpt7ZHu3annohcLb47O5QBBGkdLaRtt2bCoFDinIr2sNls0OsjGxYGgJ8++Rl9OKFgAsdxyN6gBwJX8AuJhWkADjiozUcXACWqCmR4hH4UHw3sCpBRIgzD8oyh54jalq5qlRNwer+rcGRbNNjtEobvdqz6uV5yRr771ENgRiEnY4/Mk6sKY05tgmbKEuznBRFVabJg5gMT5OVq3oMOZ7XCsHseQOH+PSgwWIQFHIc2ihO4bsrbp3oYDc72vdtwtr0VlAoOhav2A8EHcAAAK/76De18Rpwk0jk9vl/8OW6521t9KCFPeHNWcBzaQgnHihPYZlRCzbtgVqix+69/0KdH3/o8nNNKBp8gR+paamR2CYTO5/anc8VIxRIiIA3udFVeLpjIk5P9y1p+8sknSE1NRZcuXTBlypSQzskzZsyA2WyWP82bBx86jnUOHREDlTgFGKdEC+Tg+sde8xNMXf/f3lmHR3Gtf/x7Zj3ZKHGHJCSBYCEQPMG9QKlSeuu9t6WuUIOWUmh/vffW/RZa3IoXKE6hUCxIggeJh7husjLn98fMzmbZ3RgR5HyeZx/Cjp05O3POe15NmohYNyEgptLZHW5lgsakyN3LxqfUaBQFRUcm+1oaUq3GtlwgFQVSwgv/TnnxOajKSgBCkK5yQaVSOIarp0qTqlAQbnhKUSZKYkpwcOGFyVVGjQhGDnp76fHk8681yBTRsXN3AEA69cXuFUskMw0AOIkmcwPH4XyKkOhXrquEk4cnTEYjtiz/XgqaGvvPmQ6vodfrEVktBEFd8bbU+f1rxVn4gkMVpUh8KR5aDzWiEgKgE4VRACC5RpTlCc+tEUYpatpb1OQ2zp29+TH7iNZc53dZLZaRU9LGv/4r1yxBe3hIOS/VHIe1G1dZ7VNVVYW+ZcEIoa4o+7l5qkFxUglU2+dQL2pNGyJgL178MwLEJZqGcFj929JGtcP1stAO8/17QYMF3zVO07pr+17IxMwVtLvgF12sspgSq4rt54cNMAhjaCnRWwWpmalWCv1wfT17R0x8eRbA8wAh2L5hQ4Pb74iCq5dQ6Cz4iqt1ZYJbQBPpNmCU8AeloAolqj3aSZ/ydj44XFSJubNm4udfFwo5GgEE81mYMuPrFtMsNSfH1vwpuV8EVfrWu3/GwfMAgEJagxNd9LgKi6Yv+Kp1RblAneX/FZRCRQjiK3jIxMfCJb+tR6amk3ktE+pa77kCXL2+3G61Yhva3ZJFhu8cWlQg5XkeL730Evr374/Y2Fjp+ylTpmDRokXYtWsXZsyYgYULF2Lq1KkOzzNjxgyUlpZKn4yMDIf73uxUyURNEiEIRg4eevO/diPxxr3wCUKQDZ7IQN1UUJaXAByHw5m5yL9smeR5UXNJHCTGr662aJadVLbXMYnaJWVxFX6e9TT+t3AJalzdhYmK42AU2ybj6xZIXWpEARE8VF7txFskiI4MRYK3Af98/B94Ytb3GPPcR3DzapiZt9+9zyIQueCJDPI8aw2cCwTti1GhRJmYu1FZIwjx6//7CjLhOGiqNus2rYIrJ4eJUvSZNET6vjhFWASc53i4+wgCjEItR3YtbaNHpSuqSsQ0HbXO6SEGnmjbePAzp0qqIdcJpLLGCS61aXdIMFfXXhyYzlRY7bPmu98kf2I/OGPRxzfuZ2oWSA1K2+e8hmu4QBpxyvrZ8zrTuJruwbxg3s7gyiVBv+/lSJRXVtR1mBVF+9IBCIu3CXdPAABUuVsyMpiq7L9r7mKCsWzOfvYG3k34vRv6q6qdnKCuFtpd1gxm+wWf/0dK8zR21PAbOleXIXfDH3kAIdDoSuFcUQSFwVpQ55UqaXzSVhfj4bc+v2WCmKKL20t/B/Bu9QYH+haL2RVIDaJOKhAKy322N3lBpxP6Rq/Xw5daNNNX22txxIVDNaXQiqOUX7X9DAe3AjtWb5HGHrOlasvyrQ73NxqN8KqV79eHElQUt232E4ZjWlQgnTZtGlJSUrBs2TKr759++mmMHDkSXbp0wUMPPYRff/0Va9asQVpamt3zqFQquLq6Wn1uRQx6PfRaYQJUmmpw/4tzHFZ+kMnlGPvwS3ChFSgjbvDS6EAMepjUTpj/X0vkuckkCkgONKR6MQqfp9TGrAoAVBRWjEYt0hEAI5FDTgXf1drITfVUaTKaI8xNCOoYIH1fKnPD6Glz4BPasc7jHdFnwDBw1AQtb0ljAgBuRJhATUolDGJAk4uc4NiWpUitEJ6PGI39oKnakJNC/2SSMkS3jwYAVJZWI1JM6q/u4G61f2mtbvE0uMEoCg/mHKxUTLMCAG6UWHx82wClKJBe73dZIxd+8+uzItTHpm3rEckLk5mh1gIotNxa4G+f7271/4FFHbH7wM5GXet6pBKozrZCZ414f/J6NL4rfluMQFgLoJEG3wZnCigrLYdW9FNNdylBskYQApyJHOs+b3ii6XBRg1VKLNfVhraT/uZqbN1j9h3+U9LAV7nbHzO8O1hcCTKzGlYWNlgr9JlJrsTOBV806Bh7HP99LSpchHtwqSpClyFjmnwuMyE+gkZYp3FDpdYTBoUGhPLwoCVw44uhNOhATEaodBV4+vV3Gp1ipz5W/bgal97cg99mNU6LXh/b9/0Bf2r5DVWEw8ata+s8JlAvvHfBcIWaEJyFCX/LRUsH4bBqpVBEZPOODVCJ2vdqStF3chQmvt0fZUkBknuWL3VptewYzY1vljC2GygPo3g/6gzH7mSZp4uhEv1pKymFjBCc/evWVWjd7rSYQPrcc89h48aN2LVrF4KC6k5Gm5CQAAC4eLF5zHs3K3t/+VGKovfmCh3m4jPjG94J8R2FyT5H5gt/g2CaqPL0xlcvTkNh+mWY+LpN9jWiQGpv6+9fvQWTaL6VUx6RsmyM7h6EN6a/hcQwNYL5LCm/qYesbodIcxL8ShgQ1sViMjJm39hqtMuwe9BBlgcPcQDP4IoBAO2IOwCAV6gsAU0R7bHvwEEpaGrSq3WnzAKAyCrB/eOSh2WQ+nNRClwJhyLKY+DDsVb7884WXagH7wwqRtIrCAeeUpyTWco2KglBcXbDqwfN//YbzP/i62abLMwm+errItONYoA210iB1HknL2knTrjKUCBGdQdQF5SUlwAQNDQBvKChuUrKQcVJwG2tUdLi3AhKD1uho0Ym/Ab1aXzDDgkCDk8pDqqE51JFOKzduNJqv9NpqVg65wccOnnQ6vtNP28EIQSUUgyYmoQRzw5Ctagl7V8ag5Tzp+pt/9XMy/A0azprCaT9+ydKbgDO1Pb3v3jAUnUreoB9n9y4/j2kc5w4eLLetgDAQ+99CmIyCtH2qZcadIw9Nu/YLQQhUYpJdvL4NoWkh9+Ah1i5xpdeQzfnIjzxwES8+P5nePmDz/HWnI8xc/aHmPHxp3AVS5M2JyFpTlASDj11gQ3Kd9lQCnemW1kYAKAq1X7ZZgA4lnoEHpxFmXBYyyFx1gD0uCfG4jpyXlholaRYCgukER6u3sK4GTc6Alnm7BqEw77De+tsY3llBRbN+w5bdm9qxJ21PEGi20ohqUYZEeY2P4NjBVXmaWE8LiIUuaJ1K+9CScs2ktFkml0gpZTiueeew5o1a7Bz5060b9++3mOOHz8OAPD3b52I3bbiTIplUnFXNywiOPGhlxGIXFDCQeVcDeciIS1RgYc3vvzxf8gRAzkcBTWZE+dfL07+8f37OJIvAxWj7E1aDg+9+wMSJj4JpcYZgx+djic++BEv/PMpDIkMwJMf1x0t6yQKpBXEAA9/LYyiIOtSfuN1c4c//Ao8eGHAzVNcAQB4wVnyfwMnA0wmVFdloYh4wInqMOLuR+s1323avgHtiAqUUkSN7iF9z2cKZsxLCkCltvZHcw+xDH5aIoPMYNEc56IKmYHChGCeKHLSiht0j0uWzcewK7EYnt0Vh9/7A/sO/9mg42pzOi1VEgwBi4BWfZ3fpUwrPHuNEUe3bN2KcF6YDK6gCuPeSMBlMb2VnBBsWLsaALBm5WrIRQ2NrqcrUsWMA95Eg/X/Z1sKtiFkXM2SJnDvIFvBw+xXWpfGd+263ySt1AlFCYZN6wte/I1cj1m/i9d+uoCB5TGoWWqdAsxf9L2rhglBIYFw9XbGSfERUxAO5xfXnz5p57rNkga9UmXR1vp7+UMnvqWedoZl32tCZLGB8ojsbX+c9HR3l/4uz2zYcwcAblWCMKRzckNJduO1Rxv+Mxc1WsFi0a4mHx169m/0OeyhcXHDw/98GU/efxeeef8bTHr9CwTFxDXLuRuCJxXckGSEYN03a5vtvDEVtnOif5mPnT0FTuw8JP2dARMmvdMfCrUcHbr7I18UyiINfoK5vtSiade5XPdcc5b3I/2odSrG61n35SIklXSGz+am5ypubsorK+AquipkyatxTaya50FtU6CZMecgLQZFmXj7+gaY7DOvZTbKDedG0Ol0yLzWchXTbiWaXSCdNm0aFi1ahCVLlsDFxQW5ubnIzc2VtCNpaWmYPXs2jh49iitXrmD9+vX4xz/+gUGDBqFr167N3ZybigpiMTf6hUU1+LiYaEFLd5X3w6i7hsKl8Bo4fQ0gk8PoXLcPnMEgaFtqi6u7FszD39kGwT9V3EAcTOaeAcEY9FD99X/N/nsVcuF6Zr9Fd0PjfPTsUa1QwUUcdLJICQBATjgrYVChq0RxhTCAhChKGhQ0pf9TmIhzUIne4v55V0sRLRYJ8IuzzQIRPcCi7SeEwIVa2nDZORPD7h4HU63Ap5Kc+gc1vV6PqOQA6ZhQuMFvFY/5XzS8BvXSFb/C6YdCpMz5U9KwmgW0Grn1csQj2Etqf1FJSYPO77lL0I6aKEXow3GQy+VwaW9Jv6JOE67leVqYMAyUx7B7BiDq/jgpCr5vZUesXLOkwfdkJuVoivR3TPcYm+0GMYhOBoLsvFyb7QAQdMAJhBAYKY8B04bA3UeLLFFYjjBYhIFf5/+ICCpM6u2pB1ats7gbBZiExUiWzJLlY+CTPSRf0rjqDjh7+Wyd9+KbYwnudPaxXqyVEWGidLGT/MTfKPR1MamBvI6gHfMvzZU1PMXDvU89I1ZGIlj0b9uSnnVh0OtxIq9I0o6Ovue+Rh1fH54Boa0qhJopKy2Hqtb0GFngWcfeDWfZqkXwFLWdelApJVuwyfH5va5ZFsH5183Yl9WiaxGRYe2GFVIlLwCIHBBita/c22JdcCusOwNCVEkoACGKf8nyX+rct7XYsHG1tJhThfijwFWQKdSQOdRgm3OQVhLAKCoXlPXkh/5twwqY/n0J++e2jnb4j3mrwP/7EpYsm98q17uZaXaB9Ntvv0VpaSmSkpLg7+8vfZYvXw4AUCqV2L59O0aMGIHo6Gi8+uqrmDx5MjY0Q5TnzU6NWXikPLoOd5wX83oGPPAc/HANPJHh+F9b8eqX32D6O+8iRmaEU1EBOH01XPT2V33mWvc8KM78uQnL5zyDvy6Xw0TkCEYOjOIjQEw3FoCjEIUfnUqQcCtEc64r3/CUOo7Yv283CCHQwYASQlElCoHKWuOKsqYaeaIgERLVrd5zLlz4P/SsFjQVZ3wuS9//vew0lIQgEybE32VbKtWvvQfya+mbPWoF0pBYJwT5hyCLWKI+KwrrN1Mv+eon+BInGCnF9uBTKKJ6qAmH4dldseHdhmkVg465Q04IwuCOJd/9CMDyclONtd9lVBeLP+/pE3ULUABQUFQiaReT1TkI7ixoKfvcEy0Jm+E6QVBvL1aIyeCEPmjf3RfJYmYWjhCE/u3ReJeEY8K5TJTCy9PdZnNo/0jJdzfts0M2239buAa+Yk7co+pr8PAX3sMcH6GH1ESGDZvWQafTIe6sdTol74PC85uZlQON+FvneFkmP+8QN5wQlUhyQnDq178c3oZer0cHvRBRzYOiYz9rV6YCUdBVg0NVhfX77Camt8mR1+0CYhZwnPQN12wFRneBSkzUX6RpXEnB/735KoyiH7yfMRcR8UmNOv5mZcvyrVZmdV/qhIKikhs+r9NJi2rgLOGRLi5mnIgMf/xpPzgnUG9ZMHFe1uNp1GDLu6w9LoOHqEHMhQGdE62z0UTX+n9AjWN3scxrmfCt5ePqe+LGlQrNgeKsZdztPjwSXcfGS4FNOzfad0Ew5yA1KDhovIW+c68n85MxuRwyQhBl8G9xLaler0dsdQg4QuCSemsE5LUkLWKyt/d59NFHAQDBwcHYs2cPCgsLUV1djQsXLuCTTz65ZQOVGsqh1UtAFcKk4kmLGxxpbqZjqLDavWryRnrqYSidnHD/ux/ijS++wnsfzbPJT2pGXyFMYDwxYvmOwzhj8IWBKBCIXNz/4hwp7VNj/QlrU1JeIh2t8BQGrzLRP05Lb/wlM6YLk3MJhH8rOEHIU5ksgpZaAdQQFTRUh/jxj9Z5vrOXzyL+VAdwhOAiKcSDzz8pbXMuFkxgOWqZQ03UtVr6Znfx/nSUx8Rx9wAArrhYytPpK+oOBruaeRl9czsBAI5qLuHRac/C+/VOOKYQ8p32MIRYaensYU7FZCYhsxMyc9Kl/yuvC4IJCQuQXAryLtcf/LLrq02S72TPxy0JE7UealwRfxMvqLF63TKoRStAvrfltxn+Yi8Umv1NocXi722j7pd9vgxLP7YvfEcahMkznbNfZaVvYm+kyAsBCFrNBV9+b2n7vp2IS/EEIQR6ymPCu5OkbWOfGQqT6FqiOKDH6v8uhDsRfk9zdasOvAc2bVuPfb/ulPpg7BPjrK4/5Pl4VIqBVfFVEVZ9X5tNf6yDlhOeqcswon1363Q/Bc6Cxp6AICPFkhw/5dxJKEQ3CINf3S4wZsuEk6lxptYYPxeAUvByJdb+e1aDjln07hvIdRWfO0oxcMSNBzLdLDhlCL+/gfKS0LPrqxvTmOl0OkRXW4RCEqhFkdzynuT8bRvUm5aZBi9YTNJRA6yFzNjEEBSLKaA66wMlITpTZhv8Fx7njyLxPfShWocLw+2/bZA0kQAQafJukH90SxOmEyxWlTDAr70HOsdGSaqB6tP2XVTMOUg5ZwUCYoTFsh/lYKh2HGwaUCUsABSEYOOm35qn8Q7YtmeLFITWQd+4imYN4VphPhZ89y1W/NZ4y1RbwGrZtxLH9luqY3jKGx/ok/jwa/CmBTASBfb99mODjjn424+ouiD4NPLgQSiPAOShh2s5pr46F1oPL5hER2+Ob/qjcOzEYWkgDO8saBXL5MILr2pi8vXauJcL5qUCWRncaSkqRdOmE7FoC1RqYSL2IcX1Rttm/pgKF06OSt4Ej0faS9kHzv+dhUgxECh6RJjD46sUlr4y+0tmyoqk82h7WrQPfJVFo2Wv1N+Rn/bAmZOhkjeh28P9seOn4zjyn3PoqA+R+lR7qO7FQrvDwoSVjjLoeB4unBxHvt0jHe8f5jhvb00DNLgxVYLgVEiqERxqHVFfohX6miMEwQcEP0JKKYY/bCmC4eLphOLO3tLip39mJ1wrtAR/rfxgCfpnB2BAUTCWfWMdYLRu6SbJdHotyrGpbeBroyXTeWJmNA4eP4CDxw8gaAMHOeFAKcWJ6DKrRYZKLZc0uZEGbwwoFbIs6GBEWpSbJIi47KQILhPurQJGuLpZmzvdfbQ40044r4IQHPhhu902Vh8vkf4ucrZdqBlrucfmXbLse/x3wTeVUoqk++ouh2nuA+cG1LOvzcRXZoETM2mcyas7ryMALJ/9Di4SNSA+/0HIQefECY265s2Mv0H4jYtJNQrF8cb8HjSV5Yt+gRMnjIdl4DHo4VgovCxjlW9JO5tj/vz9D0k4zKM8InsH2OxzQSH8brJaQqQ2zH4ar6vivwpCsG2vfY2sX4Z1OzhCcHJF21Z30uv18BK1ttnEMn9WiIoPvxr7LgjmHKTOXk6I6hsEvZiX9cJh+wtxvV6PAGpRjtELLZsiqvSQxU/dBQqrGICmUl5ZgQU/foetb69E+SenMexKLHr/HYTDJw/f8LlbGiaQthJlxlpaNffG+yPJ5HJE+AvHXTW2Q14dvmp6XSWWz/kX/jiRDr2Ynp1SDo9OHoOnZ32LCa/8GxoXQbPBiwIpoU3XkOactrzcET0F4UcvRqPLgBs2e3gZ3AEAhc6l8FPqUCEOSM5E0MYSowE6Ufvs7eFe57kWfPoNonkfUEpxMPo8ukVbzPupWy6DIwTnYUJMP8dCnNLT1ok+w9MiYN016m6pNrmHSdC4rlyzBKff3YOMdw9g29ursOCHb7Fl9yb01Akm4iMeF5H/YxaiLpajBy+DttbkEmP0R1qm/ZRom7atR6RJEIDdoEGBTDAnx9WESvuEx4TYHGfWLMiq67ZfnTx+Gm6iFviMe77N9ughYdLf5oTz+aRaiu41k/RwFxyWC33hRGTY/4VQ0W3ZnCXoUxkEQggIIYhNtzYZu58ygRCCGmrC5EcnOmyn1kONU6K8oCAcuGUlcFmqg0bU2B7UlGDSY+NtjssLEe5fRWSQixrQSxEaDHmsG87JRLcD3kPKHJAhL7F7/XEv90WRWP4zvircppwjAIRXWEz0UUmhNttDOlmi54tyLBof7wLh2jWw5MR1RJU4QTeknv31eFYL16zRaJGecszhfms/mY0zeiKlhtPQKgwY0YRyVTcx5kCZbEWZ9Ny7USVOHj/dpPPpdDpEpll+/4schbObGh37WRZ4QXrbgD3XLMuiO4ez/66GJFi7FhnBo8890Xb3pR6W8+WftK/J72AUNIQl0EMvjhRx5ZFtmipq0x/roBDHxCJ1rXsQ/cC9eFslRO0cpH4RHlCp5VKkfWaK7VgGANv2boWSWMSi4PLm11rWpn25xVLKEYLfNzWtFLFer8fixT9j47tLkPP+EQxL64zOJj9J+yonBGnrjjdHk1uUm7+kxW1CjZPlhYnoObhJ5xj2xDs49+HrKCIeWL3gS6iJfXNwJVWjkPgBBNBSYVKllLMb6GPOTcfdgEDKFQjn4CmFk1YYyH3CgoDj5SCE4O/kvzBswIgmn9+T1wIcYPIliIsYi9PbhFWlN+eDi8Z8qMtLUezhJZQrHPmgw/Ns2b0JifmxAAEOqy/jkcctwVqGaiOCdSYAHMrd6y5HF9CpHZBnqepEKUXcBOvI4nxSgSC4wptXY+mHP6JfeZSkwYgx+SLmki/4NMHv8RrVIbwkAkFEhnJKcUHGQxvpgZL0EvTWcZARgr0L/0D4jGdwPfLd1VLdajeigBtVoJqaJNM5pRQefvYKIlDIQKCsqVuDfXV1CjyJv5DE/YVJNttj+gUjdf0FuNUSgC6r9LAXhjJmRiLOzt4LL6jQszoUK2YvRP+KUBBCYKA8FISDG1Vi9YK1kvAZanIDCHBRXghbj15rxj0/AAff2YkwaAThWHykz6EKk96xb06e8NRoZL69X9J0n5EVY+STgqYvckpv8AvPgCNE8pEuCbUfLCSXy5Ebo4XnGQMUhMPu/27C5FkPS9vTMtPgR4WSh9nQo/dA2wXPgF6DkLv+IDhCwNcSaP1MgrariNSvza4keoACKtp4y8TDb87Ef7/6FiAEvyxZAbnuF7vZO2q0roCoaVZRHYb37YHo/qMafT0AWDVrKYKr3WCc6I7+ferW/rYWuXkFUvnVCj8eEx6bhIL3j4AjBFdXp6Br906NOp9er8feDzegM/wlrbt7J0ELGdk7ACd/Ow9PyODOKXD45GH06tpLOjaoxqKVNbnZH5e6j2qPi/sypMT3GdAhzMN+5HlYb39gq5BJoV2xrb/wpm3r0Y0I31/kTCDg0ZNXQ0vkWPLrfDz65D/tnnfT9g0guysgG+6OkYk3XoL2eipSCmAuTd0u3GKBuuZUiYgKTzjZEWVq5yBt310UsgkQQoHKfPtBUAXHM4BaJVoDqZvd0sjNhS/VWqU7UZ1t/Dz86yffIr4gGolcpPAFB+gpRZr8GrJC86EoJEgs7Yyu5RGNLpXc2jANaStwdu92oX49ADWtQmTCsDr3X/XzGuzYYuukLVMo0aGdMNBcI95IR4DdTyHxhIrWIN6jCjRYeBF5B5WcDJwg1DoZmp5UWqsTBkpDrWCfDl0sK//ss/ZX4g3h7OWz0Ipmrk69eqBT4njUECH4ox1xxxtvvoGoEGEg9kEhgqJ7ODyXaUcZFKIAOOQ1a/Pi7oWn4AcOlZRi0CNd6mxTzIBg6GtN1DwoYiM7W+2TrRK0wu5QYWBFNGSi9g0QzKoGMQiHUgqecAgiMhSABx3XHnd9lIQhj3VDv390lYJU4ko62mgodu7fjhijMEhzhOCqeF51rWwOFLDrC2s+r7qe+u9RotYmm5TDycn+M5ImszZrRQ2xny1D7awEHREGXrz3fpVhIISgmppgfCIE1VTQhnY4K1xn6efLJHO701BbU+X1yOVyqAe0t3rWS2FE9BO9HPoDy+VyXJWVAABqqAm9X7NU6wqN9cYphcWXk6cU46Y69pMc8Ugf5BBBY9NTF4ri0hJp2941WyXTa6bSfluUSiVqxN/F1WgZml1FDXWOsn5TepWY5ULRBFcZN28/OFUKbTYp1ahx80C1u6fNx1yqU0FrMDQuCnGjHC8C6yI3rwAJukAEwgUBa4yY/+3NUYN+97Ltkr/wmKlj4OTkhGwxUDHKjhbTTH5hAZb8eyEysq9K3+n1evwxexU6myzCaB549HvAki2idsKflN2WoLxrhfnwqRVcFBpn32VALpfjnMxSkS/f2fGCutPAIOhEf+dgO/dScshi7dKEeCF4oCVFVUSa/bgHvV4Pv20qdDUGQb3Fvhb10qWrWPZ53b7wdRFcKlybgqLbUEubfPoI1h+OEOzf/bfVMbVzkDq7CfNmjehuJXPgQ+pfbN0nSsJh846WCbj+89BuSRtrrpgYUt24fLpLV/yKwYWd4crJYaQUF0khtgWchOb1SAyfcw+mPv4U/CO6w0ApXDk5lv+yoLlvo1lhAmkrsH/TRulvb1JaZ63lxZ/8goRz7RCxCyivsJ2ARj87B7299OiiKXT46eZchHvHj8S4Fz+RbLPUgUBa4FkCwP7g1FC0JvFlr1Wi0j/KQ8rzqMhr+mN2eN9+AICO5yXNAS8XtEfOUEDj4orCcmG166l07KheWFqEKL0g1JwIuQh3F3er7aY0IWDmrJyXorAdoXZWWpUQrSS2vo1qP4tAbqIUOpjEwBoKFWTgAJzmipFDauAHFYopDwwJRqdamjO/9h5IEfMMuhElVqy0rgZk+r1EEnLSYELH5+JwJkgD43XCsj2MxCyQOjbtbl2/U4osTw91nBhcG26510oYEJto6yJgpseQCBzWWFIz6SmP8ns9EdmxPY6KwWBeVI1Nq7ciPMdNPKcRA4f0dXjO2sSPi0SyeG88KPKiPBEYWbeLTMfn++CYIht5Y5zg7mnti9bvpWEwif1ZSvQOhXIzuqR2oJRCTghOzrMsKgNzLe9XSB/HwnW5+Hu7iz6gSz9aDLmoQjG1d5xv0Uy1RizUAIIN766od//refz5l+BaUWBTpvN65NSApE6B6D3h8UZfw8zeH/+Q/JwVhGD41Vj8/s4yXM28XM+RLYtbvjA+68FL/sLm518DGbaut606VlVVhfz/O4FB+WGgn1/F9rdWY/6332LTnGXobhDe6TLRopWuJFYLpBony9/etaqcbd20TqrOVUx5xA5x/F55xlq2RQ+2XzgBEITXDPEZ84DKplhFRLlwHh4UPcdHIG50BNIh7BPKu9vNj7zki5/gK/rzd+A9sOfvXTb7VP1wAQNyArH5nVUO2+aIwtIiBPOC1rYIBqvxOWnYAGmeydxnXVindg5SMwrR+uXqICtakJglpPa4WXbymv2db5CM7YIbFqUUFzhRyUIbrhjS6/WIPOorvUMFxIRihSe6R49GYaoBa2b+iSPv7EPHowYQMd94+FXHZbRvBphA2goU6SyrRhelY83F8TPH0b+wPQghkBGCDV+ttdlHJpdjzHMfYfKbXzr8THr9Cyn9CjWJ5nRiXzDpPFQQ8jw4ZZOSsQOACy9MlFWwuBDI5XJJY+qmsy/g6XQ67JmxFntnrLMKcrEiW+i7Is4iEHl2FUwTTlBg328/SemewmJ6OmzjhkXLoCYcqimPu6ZaV5K5cCgbMWIwU2D/hr2wxbUsK7l2hD6fUEGbYaIUaUQHDWTIAg88GIlkYoKMEHTiPRAANcophW5AALqP6GBznnbxFgE1MMViYlv02QJEi75e+ahB5JNd4R3kijHP98IpH6U0oBY7cOswB7/UpSGVHyyTzOn3PeM4TVnC5CjoxfOd5RwvCsxMnnUf0rhiFKMaeeOc0CNe0KiOf2US9FTId+p7mAombgBnVY2bEOIf74pTMOGYqwxDHqs/BZi/vy/umn0/+iX2ttnm4eWKv9plQkeNSA0srPdcg0b2wjmZsF849cC69xYJ6Z4kvzwDuo9wXCykVHzOtVBg6f8twYDSYBBCUEkNGHV/3ZYVAEh6fCRqRE1zD4M/Tk/fgT93HkBVVRWW/t8SHJm+BVfe3INtb9uPHvYKbY9XPv0Kb8/5GBP7d0a0PBc+tABetEj6+OEaEqN90P/+5+ptT11EVgimV3OmAwDoagxE2ReX6s3n2hTWbFyJo2/+gfXvLayzapifUQhqKaoVPHPfM/fCID6bzgdsU2/t/eh3KWk7RwiieR8MvxqLXjXCb12MGrhBiSLKI/auSKtj20W4S3/XTvGkvGLZJ5PQOvPP9pkUhSNyHoeVFJ0GOPZ/B4BijSXh/+btFu1fZk66lN4tCzXwChT6odhXaB8hBEXrr1qd62rmZfS/ZnFhIISg6Hfr4gorvl8l+aF3NvggN68AjWHT98skX0h7trYqUePrXWU9z9TOQWrGO1wYQ30pZ1PWOTMnXcoBfJpY5pvgkrr9SFfNWoLLb+7BhneX138ztQgr9xXbZ0S5nzCHKgmHw8cO1nWYxJKvf4IfsQiwfpCjl57Ae2cW3DdfRa8aIEhUKJiLpITy7ti5337Q5c0AE0hbgZpaUd++oZF299Hr9aj6JVNaEQNAdInj6h0Nhjf/Y18g7dW1FwqpsGK+uKdpqT00Yn3vyuuEHx0RXnh3k/1V34pFvyKctkMH6omDn2+xu4+HWJe+QFEifTdk1L0wgQcHDqdPXICeKOFEq+qsWx+aJZh8zity0M7NWmOWsvEiZGIwU/wY+7/P9RhraTV0xHaR4RvqDkAY9DvCCQZKQXv7oUN3f4yfm4STgWqUUh7llKKopzd6j7d/3b6TY5AjBsuE8+2w7/Cf2PTOUiTmdJCERbcHYhAQYbmn8a/2QbK7YMJJ5+xPYnqYsyA4nuQ6mITB+5Ks7qo/zm5qpHCAkVJ4x9U9GZpJ/OgudJk3HH0HWvzlnJyccEwj+Ob6UifJpaHz1IQGndNMYKQnRs9LwsS3mqdi0INvTEHkx4PxwPP3N2j/xPfGo1BMh9VTH4ptH6yGWpxQL8hMdQoWRSphInSmcgwoCJICuowPB9arnQUAP18vqF6IQjopBaUUrlAidKsB+e8fwcDCYPjBGXLCIcroVW85zO7D78UD73yHZ9//Cs+9/4X0+desbzDwwRcb1BeOKCstRzsxcOioJgcZ4oLGRCk8OCVOLtx3Q+e/nk3bN6DzXm/4Eg3i9GHYNWedwyAdd3POV4V1mrHzckGQCuXdsPFdSzaIFd+uRGeDeXGok8Zas6WiGDXwgAqllIc+MQCR8dam7x6jOsAgHtMOKqx/dxHWv7cQnWpVdKp2rtu1RqGWY+KHiZj0waA69wOAoG6W61ecsSyytq3ZKFlccuQWn8mkx7tKQl+cIRSLPrKkVTvy0x5oOOvxr1t1qFUga8xlN0mLxxGCI1/ZalAdodPp0LPAEqBlcrW1EpiVFT68dSAlVysH6YL/fI2FH38L6lMEnlK4EIKss9bj2o7NWyy5Z4PboUAcdwN5x7l5V36wGAm6ICgIh+56fyyf3fDk9r5i9cFMrhyDH+gruXSd215/4FzmtUz0zbEsBE74qpDsrUAqTDBSChOlOAMTjnnKkd3fD5XiuTlCUPLHzVsVigmkLcyyD96SEkeD8ug69B67+y3/7/8QAsFEadY2eVMNcgrqzxNZF4QXHkRHJnsASFcKqaECi5smAJtTO5XX8mMCLLlIXRyUdvO/YhGi4vRhWPD9dzb7eBnECjXOlslBo9GgigqDjZEKq1cfUuKwVOjZy2fR3iRoUQujrLUbVRXVCK8R+qbKp+HO3q61zEYGO2+RX7iHZOoFgONaDn3utlTnGvN8LwS/Fg+vad3Q/z7b6kO1yXYX2sURgoBVPLoZRUEFJqQPVqFDd1ut7oTp/aB4vDPGfjDAZhsAVIm+w67Uvr/Ziu9XQSH6bxp61Z8Ye8Tb/aB8Khb97qn7XupjzBsTYBQ1UYBQmSiyY/3lh28mFGo5XB6IlSKUuxkt0dXeXYMcHQYAqGknPDPmrAN6yqP0bnd0jm14ZbegQH/0mzsOf7bLgEn01zX7MJdDLxUR2PhL29Up3/TdemnB0W1qHygGBEJPqRT416Mi0mE+18ayfd8fCP9DK6VcopSiqzEQmz9cbiOUZlzNklwk9GHW48nAt0ejADoQQtBN74s17y1DxtUs9L7iI/lCh8/oi2R3QXiUE4ICGOEBFSooRVmCr90Fr4unEzLEZ4UjBHGGUMTpw6zq1/t1dpzIvrF0H9lB8iEPKrH4pfpkWwQvz46Wv53d1EiRWxQlSWWd8Osn3+CPP7ciXswSYgKPQ6L/spJw+O37hQCAtYs2SAJ+hVjQpIveF2Wl1u5oVVVV2L/XtqjF8u9+hhunkIQ1n4627je5KuFc1+e81kiK92sYdq0rBhfHImCtXsoxnLL/vNX+blfMGWkoEibF4IqY1UBDZHa1iss+Wow+lcGSvzEhBP0qwvHLv+v3hT559jiUovhV4iOHu48WOtH1K6ik/iw8B779A87i83wWeox8Pg7jX+2DkfOSoHm2GxRPxWL4vCTc9UZf9B4fiQu+lnG+a3V7u1lAbgaYQNqCXDywB+erhZJ8AOCFYnj42WqQ1v6+CgOLLEExZ4LU0gO++wf7msMGI1ZgMsGB0wyAQj9B2AuuVXauMZgjUqtU1iaQMk5MQUPtREBey0SEURgMr1HBfNYvLRqHTlqbKzzFdB7U33oVXim6B2hFYde7neOX+O9VOyEnBGW8Efff/7DVtt0/n4In4VBMeQx+on7zrpmIPhYhkCpsNaQKtRxl4sB3BiaMe902w4GrtzO8Q+znC6xN4uPdJOHG7ARfDgP0d4UjcZTjEqmBUe0cauMK25s1pBx+m7/GZnvoZUvd9lGT6jcVq52VVlrapuLk5ISTSouP6WmXlvHfamk6dPfHmQDrhZgORiRMqFsD32mQJSDMSHnkjFYjLqF7k9ow5Y2HUPqgFy5yRTiuyEX+3a6ImTdUepY8M9ouyUp4sfCslBMDwiPCED8uEiddLAKjhuOw5+fNN3ydfYf/hP96uVSQAIC02OmpD8P6udbFGP5cuccS0PTgSKttTk5O6DCjL0pQA0II4msCoPvmvBR4dyZOB1c3F0yY3g+HtcJ76gU5dJQiv3s79L3bfiomACiSiblGUYlkRTqSFek4IxMUBZWUIm6MY7/QxqJSy5EvavDDTV7Y8dZqzP/mG0SIidkN4NFrQkerY/o90Q1XqcVXfnBhLHw3yqQFxDGOYsyMgVLAVFye8JyHpCglLX/xOBdQcdGx89+WxVDykZPIef8QQjZVY81MSwJ3vV6PHrnCQowQgoswofdE2/dHFiVY0WQAdu+0uJ25iguL0Bphu15ciJmLTHS4VGOV9zNUjKMoIXp4+GuhCbKMZxkHrYXXJZ8sRn/RnUZHjUhPdIURwkJ6yLXOmP+1/UI1ZlLWHJees5H/ELLu5IoBkX6muosE7T6wE72rhOeBB4VnYqjVOO8b6mYzFo97JQHHxTlTTThs+sE63/PNAhNIWwiDXo+VK34Dr1SBiL4qbtdFIwNCjs6wPW6Sqf48NWLsc31QImoXO5XVrVGpD3PsDXXgQwoA/ccOB08ptJwcm7atb9T59Xq99BART2ttW7lY21Np5zHbvngdlKLJOY9wqKRGOHEy6JfkQ6/XI+X8KSz8+FtJq9E1wdq/r0xmNm2qQCiPuNEPOWxjVGEYAOCM01Wb9B3KbGEQuKgkUiRmQwiN9RYCkQAQtX2/4ItqDhdgQvgDUVComz75u/tocZKzaHJKYIJhZIc6c6XWxz1PTYaOGkEIQdA56/s+9NdRBFBBK5raSP/N5qDfyyNRDj3KoceY5+5q9es3F2NfSMCRWgFvZ0hNvc9B965dUQgdqqkJFwfy6J/UOHeF6+navROSPpqAcbPvlQTbHLEYQJCp/sVQS1BVVSWVprygtPgTjpuegDNi8B8AxJdG3ZAm5+zls3BdqYcbJ7gUmUCR7K3AGVh+kz66CCz70FJoxKtIeBeqYbLrIuHq5gKvl7ujHHoQQuBEhN/ziCob4++3pDua9E5/HNFyuAoTMqLdMPDBzjbnqo28ndkPX43xsx/C+NkPoVIjmNbTCQ/VDYwf9khXCvOBjBBE8T4Ynt4FzqLQfglGm7HQr70Hgh7ujCxRKCWEoB0RNZ8woe9T3aB2VuKEWni23KHEoi8WwJsK1p2j2mz0HdgLWUQw5Xev8UdVVRXOpF6E68oiqIhMEPKrg7Dw4x8AAIt/+km6BgAo4nztLrCHTxgMo6jAUf8hXL92DlIfogFPKc4ML8XBvlnIgtAGF6LAxi8Fv0+9Xg8Psa3pMmFOiBsXIVkWAwstmuSlXyzFwMJgizvNP4LRf0x3nPJXSH0zND0Wv/z8g8P+DykTlD+VxAgXT+E5y9GK+bUhrzN3t2lDca2FAI+40Q1brCQ8Fy8tGPoUdLTxob0ZYAJpE9jzy/dYMONVGOpIFPz9Gy+jxs0D4HloqfCAu7u62+y3eulieBG1ZJIwhQv7nBdfbE+qwvEzx5ve2Hp8SAuyynB+TTHyxUjKoqNZdvdzROq509IE0r6L9Yuh8BImPI4QZF6z9lsJzxOEKQXh0AUqOBM5KKUIgzvOvbsXLv8rxuDiWABAKW9At+huyE8vxe9fHkbqngyUK4Q+1VIVAsg1+EfYT9X056HdCBSFK1kPLTZ8chBr5+zH2jn7seb9fYiGDDyliBrZeLNwhmjS8QyyXyVk0vsDMXheEjp0b1yZWHt0GBYJPaUopTyqBwWi6+CwGz5nikrQwPhTZ1y6ZAlWqN6YBUIITJRiyKutXw7Sy9MdMfOGImbeUJuqSLcaQ17phXSYwFMKj64NW0B0mzcCER8nYdi4puUrro8MNyGi1xlyG9Npa7D+x/WSC0HIeIuLh1wuR7vEIMmk6szJsOl760CRxYt/xpLlvzToOpk/psJTNHvzoDjZTonxr/ZBr1ficaGWUDqgIhqL5wruQn5iAYS6cr76+XpB+0w0KkW3oXzoMOmDB2z2m/hOf/Sfl9SgwLpwMfNCMOWk8clDLDtc4WDBeyME9wqXotMrYZQKeQBAmca+G09orDc8741Cbi1NKQCcdVPCr71g4h/14gjJ5WZQlsXPfdyrE4VrJWrFLBQcNn+yDvJfM6AiMlBKpYDGpKJozP/6G3S5ask8nExMDl2bBKuKsLAJpFqsWLUYWWeLoa6Vau+CLB9jh43HPRMeQG6t4il9S6Nx/Mxx7Ni7VVIKkTDBPcI7xA35oh9psEnQOK5auQz9swIld5qyezwR01mY98a/2A9HxTgKjhD0PdvRYXUk83OWKSuTvosfLyw+CSHYuNy+Ymjx4p8RxQuudVUwoddj9lPs2cM7yBUnAgTfcg1k2LjYfqWutoQJpI1AX1WFz59/FrvSsnBF5YLPXn3JrlC68+dvUOAqPMDa8kJUyIWHL7yHrcO591VBPU8IwWWYMPhxQbCKv2eAZLY/t9Jx1ZT6IJJAamuyN1QbcfzLY+iWVwODqMkx53urTV2T1sXDFwAIZrZuva0HjIhYy4CybbGlAsWx1CMIpe7S/8/AhGuw+A16EBVkhKCG8kiR5+C0ZxU2T9+N8q9PoGtWNao2X0K5UtSQmlww4bFXHLYv848LIGLuURz2Ro8iA+LLecSX8+ilE03qhK83MtUevZ+LQ3Z/P6ucgi1F1yFhqBgeDO3DnRoceFUffacNAy8+Y6fnC/5bZ1Ivor3JHQCQqsi75QXCtsbV2xmRz8Xh2kD/erVkrcXgx0ZKY8umH5s/x+KZ1ItIPnJS+mRmWfvBh+QKC8QqmNCjl/WEGjc6Askyy1gVXxCF8soKXLhyAVvfXoHEU5EYcKw9Vq2rO6flL/8WKrKZOeYmx3jRbcbdR4vYZ3vgUi3BKrG0M375+Bsp52u2sgx1ERwaCP+34vGnfxai3rPvp90YInv7o5DykBMijU/hom++h6ikaE56j49ESpAGpZSHM+QgIFJWFL9Yx65bkfH+cBofjgJRgL1KTRj9ssV6pfVQ47RCCJQyB0gd02RL2uahowchT4xg760LhEZUROxzy0BWgrs0Hg1Nj5UiyHXg0eneun2o4x9Jko6NOOyNqyeFZ85sFi/pYwm49QwR5lxKKVSEQ+aS0yg7IGjieUqReJ8lW8tVuViKl8ixZNl89DwSIBSuoBQZQxQ2z2/iy72RLWohnTgZapbk2vgpX756VSqHXOhjedbDuwbDIPar6yVbTbBer0fnk5Z56rSLot6UdtdzzwsTkaK4hgNh1zDxkbGNOrY1YAJpA/l75SL833vvoLidj1Qyr7KdD7549SWr/TZ+/jH2X7gKcDLIqypR5eoJSjiEIRudEq1LF+r1ekTUWATA8kBnySQRGuuNYtFsH1tpW2awoXBiOiN7GtKNnxxEtDjoeUIwVwTxHlYv0IEZm1D6UTLWvmc/pYUxV1hBUsDGHNmhp79k8kjMjcXuA0L+vtPrj1jqM4NHnzd6o8us/jgfo0UWKZd83FSEQ6zRH31L3NAFMijFY/zBQSlGVGqpE3xCBX+npSt+xaU39yLljR1Y+f7P2LRtPWLKwwAAlzW56G4Qjk+BCSeJ8DlOTPCtI79fXXgFuqL3+Mg6o6abk67D2iM0tun5Yq/Hz9cLmWL+u07mBPiLT0mVn7o+c+MTLUPQTMSPa55FRHPg5+sFnaghDCiyDlhbPnsxMt7ci8tv7sHut9Zh3dKGBz7p9Xocnr4FLgtz4L2qVPrQLy5g91vrpIWtuQzrJYX9NFq9Hu2KMnOBAE6BHZ/8BsM3mehsEsZKjhAEH3B1GCW/eedGJF2Llf5/2Jlg4gzrKlDeIW4If7IrzsJithxSFAuZ6HfIxdS/EHN1c8GDLz7QoOwH9SGXy5EbqpXGJfPnsJJiwIMts+Ad83wvaB7siNMwQUkIFOBQTinixtbj5zwgGIoxHXBEzsNlZJiNO0HsowkWix+lGP7yOKvtOT2otCCilOKgcwYefOshDLy7Gw47W4K7zKSoOYTH1W1lCojwRKo52p5oUHj2jLTtMleMyXdZNNh9JkVZuYZ01QehU4WgoS4nBit3Bbdwy3X7J4dL5UsP++qQONK2spi7jxbcoCBUie9XGNyx8lPryPuDS/Za/JQfs65wVihq5oOM7jbnXvL5T/AiQtvSoceol3rZ7NMQRn94D+57xn5wdVvDBNJ6MOj1+PaV57D55FkYtK6AyQSv4nx4Fgm+deXtfPDZC89AV16Gz55/FkcKK2FSaUCMBlCNCjyRIRg5mPLmf23OvXLVYilSLhcmDH/KusrQBZXgU+JGlNiy2/7EcPD4AWx6dykWfPa1/RvghRfoeh/SA6vPoHul8PLrKYUT5DBRCjXhsHaDkFB74Sc/IJi6ghCCnjX+WDVrCa7HqVoYjIx2NLBOWjXOiQO+knBwWWuATqdDpxJLvs1cfye4eDpBoZZjyCM9kDB3DALe7Ysz4VokExPKKcU18Dii4JHV1xfJoibXTSeYiLS1SlaGHPOEkhC4c0r01UWi2w4PeBG1UFaz2h9yQpAKE0bNS8KYucJn3Nwku/k/7xSKeggaCiWRYemnSxApZiM4LytEUOCNuxowbk7MpsIAk0XwqqqqQu8KIYODgnCI4D0Rd9wFaW/uxsqfVtd5Pr1ejwMzN8MfzjbbCCGI4D2R/9FRLJuzWAoCUg+wX3koMNIT510sQk58TXu4cnJJyAGAQLhg8Xc/2RxbUl6CkK0aycfub6URk961v7AKjPRE4qyBOOTES9o1s6AwfELLuEvUxchne0rjkvkz6YNBLbrg7dDdH0M+HIAjrhwqKMU5JW2Qv2rnxGBM/DARXYeE2WwLiQhEqjwflFIccs60sbKMv380skgFKKU4rMnCve9Z/P8nvDUAp2vlsy6AEcNecJxfujauPSyKm+41ltyhmd2s/ZAVajniX7S4bXCEwEMU9DJl1qm+ek+MkpQ5Fr/NSkx+xTrgrTbxYyKRH+craZwHlcZg5SpBo7/5t63oIlohK2GEi6v1YiZDJVzf7brMNFczL2NQgWBhMYKH6+hIqJ1bppxpW8IE0nooyUpHvkoLyOSQV1ZgSGQIRkyZhIQhcXArFHzwSjx98emcD1Eiak+VFWWQyShMRI5A5GLKq/Og1NgO1M7nLC9+hkpmo2HsNNLie1S1y1abcCz1CJyWVqKbIQjDcrti51u/4cTZE1b7mGvU1zbZF2SVQXb4GhSE4BRMOKEW9jG/QKazVdh9YCcGFgpRoebVbIIuCCs+tBZKXYzmKk32o/ijH+2CPAiaDF/ihP2zN8FHrOpxCTUYOc221KfaWYnhT/XA+LlJiPl4EOLmJWLi7EQkTOgITYQgiIaIOdzUhENmTjqWrVyIULiBUoq/NBeQiXJpAssjlYiGGkZKm6wNvV0Zd98oVIqLhv75QVIqntBHu7dtwxgtSp6voIlRQ4aMq4Lf+KZP10qpvs5zRdCLCfZVRIbuF+o2Df4xexXCqbDPZVKK0qk+KJ3qg6IH2iFVdk00j8owQKwEVAMeg0cNdHi+US/1Qjas8xoTQpACkyQgDMjqZFNQI3nubrgRYaI+JivDhPccXwMQhJO730tEdj9P6ESf0ApiaBat562CXC7HxLf6I/z9fpg4O7FZzjlqzmQEfzwI9743xe72PvPGoN3MeNw9y7rsrFwuR9TULlLAaHaAVgr6qY/+98XgqvjMmCPpS6keUx54zGZfD3/BbSMT1lr2Mn/rWvJaDzXyYPEnzoYeg1+rP9dr//tikNZRKy104g/74+KbuxH7twbuEHx008WSxbXRdBMsVXJC8Oeev6Tvz3x3WMqwcsydr7Ma3q0ME0jrwbt9BDq7quFSlIcoz2qkph3Hks37sPl4JtzbmaAtFDSlJrUTYDLBtbwAJmcNjEQJP1zDfc/NhMbFfmLdjjpL6qDwJFsfxph+wSgSnarjqsPwy6ffSGaq02mpIL8Ww1OMQqSUoiPvDfXPxVjwgyUPmlkgNdUSGP/6+hgCwKEAPLo+0RXdJkXAWKsGemh5AJzX6iUn76M+PCqpAYQQ9C0PwtJ5llQpWrHEoe66ycNMULQ3PB+IRbm4PZr3ldqr7B3c6NX/wIc6o5DycAEn5fk8evww/I4Jq3BCCKKrwlHW3h9X7yHY7nMKVBwAjsvpHa0NdcQJreBvZTaTXZKV3HK5PxmNY+yjY6WF5v7FuwEAcZWC2TKPVGHIRxPQ4eMk7PPPEoIgiNyhlnTlrJ+l8pil0KP760PQOTYKnWOj0LV7J4ycMxkHwq9BXytw5ko9xRbUzkqUhVuE4FLK42SAGqPmJeGIUhgTNUSG/V8IafHOpF7E0elbEMkLGv6LpBRj3h/Z4PGlz4RYOL/YCYeUWUjvR+o/4DbkRjKBNAVHQn9orDe4MR1wJlyLMS80zixd4GGdS/q8yvFz5h3ihuAnekgpBCmlGPOIreYzXSXMHzWg4AaFNFhAHvZ4TxzyFRQjCsJBLWYS0FMeZ7kCdJpmWw551N1DpGCzsN+NOD79D6yetQSdDYLG9yqpwF2v1b3IupUhtLYd5BahrKwMbm5uKC0thatr3Tm7bhST0Yj5H05DFvUFFVcooBQEFJRwcKYV4Ep10BM5nJ1MKFIIEXoByMM9T0+HZ4B9/8/V65ch4S9BIL0EAwbNG2J/v/d2IEFvUc1fJIUwjnWC+wZqVTYMAGooL5VYM0dNykAgJwTnuHyYgsNgSC9DNypEll+MdpMiQH+fvhtdYRvNmQM9enyYiPT0LJi+vyQ5oecTHc65F6JLiQ/cocIlrgSDPhpvc7yZ0/uuQrPhirRyvULKMGBu05yq176zB/FGDnrKQ0k47HFLxaCSTpYqGyJHORN4lRy9dEJ0usdTXZolV+btRlVVFfLfPyJFPuff7drk3JeMW4fT03fAFUpkoAxXXEswsCwElFIcj6u0SmF04c1d0BA5clCJXvOsfd4WfvytlA3DBB75AwMQ78AHMTevAOc/OwAf3hnF41ysqnQ5Ys2sPyHTmxB7X5SUraKm2ojTs3bBG4I7zn7Xq+hXFiqZVHNRiW6zhrW6gMVoe4pzKpD32TFoxWfhbKQrhtWTX3r/jkNw31aOq4pijJlt61uZeTYfKQtSwQc6Y8zzjffbXDF7CXpU+COXq0BehBH3PDGpzv03vrsS3fS+NvOZkfKQP9kJQZHNF0fQGjRGXmNvbD3IxBU2JRy8aQF8NRTdh9+HvLQUHEpNQylxBdycoYIeRUQFOTUixqkUE1/53GHlIADgky1l+4pc7KfZAIDooR1R/ftlKYVFBNqB30CtnL5PEhO6UhkUhOAyKUJ76ikJpmbCeE+orlYCotB5VAVMqpWORN3RAzhfJmlNzFzzdYFcLkeHDqFIfbga3MIcqIgMPnCCT4lFIL6+StP1dBoQiq3ZVxBzVFz/DGv6S+Xfyx84kAeFGIDQpyRGanMeeGTIKOJNMvTkZYAYSX/RTYEJTBi1i5OTE1IVeehq9MNFWTEGJ9RvkmLc+mTIStDZ5ANf6gyvUieACPXbawujAHBKk4ve1UHwo064cP6ypD0/fPIwBhR1hvgaItlVjol1BMT4+XrBb67jRas9Js2y1Qap1HLIx7YHvzEbHCEYWB4GEEHD9bcmE/fMsm8mZtz+ePhrsUfOI94kzHN+EY7LfprpP7Q3MBRwlAMjKNobQfOSmtym+94VnseGhjWOm30vcvMK8Of3fyC6ygfuYuaHg+1y8EBk87hU3Kwwk30DiEsYhMlJ3THt/a9wz/SvEdEzEf3vm4ZH/vkiOpAsgBDUEBW8aBHG94vF5De/rFMYBYBoMXKeB0XPiR0d7tc5MRgFCb7IrJUWqbYwelgFjJmbhGOcCRwIXKg7TvWrwqH+OTjgnI9K0XFbBRmKKY8jMhMud/O0qXk86B+dkVXrGgCQTU0Y9kycpS2xUXB+JRYH1ZkoRY1VkEG5k+OcrGZG3pcI47NBKH/YFwOG9a53f0f0HNsBGbWiJGsL33mBTpg4Jwlnw11QKGqJ02HCyBfjm3y9O4ExH96Li8MIBn80oa2bwmglyjsI740CnGT5OB9hm+Jt0AujJV+4cwuPSN8XLr8sRR2nwIhxb9xYEv/G0GVgBP52sqSTqqAGnBtoYsIoA6GDBPcRHaVo371p5bDbGj9fL9z73hR0mTcMhn+GInOMGg+8YZvr9naDmeybgZ0LPkZpfjZG/WuWQ3/R2mzZvQmxW4R2n0U1hs0bXu8xZfmV2PnZEcSbLMLXEQWPcTP7Qy6XoyCrDJe+TEYAOKTABBMBulFhlXgJJujD3TFgSqc6I/PWzN6HXpWWx+GYhxx3vWnr52ImNeUcLq5IhozKMGTG2FYNBFjz/j4pj6iZczAh8cMBkt9Y1oUiHF5xBp2GhaFjgm29dwbjTif9zb3SArcUNeg8z36Z2H0zNiCMukNPeXT4OBEr1yxBn4NCRH4FTHB+uDOCO7e+KfG3mcsAQjHqjQl3VCASo262fncMCpWsQUUJGC1LY+Q1JpA2gpLyEqxZtARBmT7Qmpxw2SUTISNiMKCXYFbKvJaJHQs3omd+BJyoAnrwMIAHD4oKUo00z3TET07EiSV/YWClEMF+wleFsS83XFu446fjUF4sQYGSw/j3+lk57R9YfQZ+h/IlrYWRUiSrgJFv9IaTtv6ymNkXi1D1YwrUhCAfPDrOSGhUOc3W5PzfWXBac0n6Pw+K3P7+6D3+5sn3yGDc7Jycvg2eEN7xff5ZeOBF+1qYzb9tRezfGhBCsNcvE51yvKSciGfDXTDsqe6t1WQGg3ELwQTSZkSv12PZkl/gfdEFkQZ/qK/zzaSUIptUooKrQrjJW4pMd4SJUphAoSQcDODh8UpPuPto6zymMaz5YB96VVHkgEd1N69GV4dZ99Zu9ORlOOIuw8Tptol/byb2T9+GUHEyPUpMmDA3qW0bxGDcYqx6fyn66IJQBj06zRta575npu+AC5RSMCEAHJOV4645rV9elsFg3Bo0Rl5jPqQNoPeZCHQxBkJNOFRTHinybOx3PodrVAdCCAKhRRTvIwmj1TAhlStCwXU5ziilkBEiDebniK5ZhVEAmPTeAFxL9Ef0G72aVKpw2Jt9kDvA76YXRgGgwEPouwrwiJt6c5RlZDBuJe6Z+SD+DMyGyzPR9e57UpsLANL4VQEDhr5hPzsIg8FgNJY2E0i//vprhIWFQa1WIyEhAYcOHWqrptSJUqlEJdGDpxTpqERKOw7xz4zG5BmPorpXMJJlZaiGpSbyMRjh9mwPjPxoArrPG4rcgX44rKDIuy5gCAAU4X7XX65ZiBsd0eBcadfj7Ka+qcoc1sXwaT1wWEmRGeneJv5rDMbtwIPP34/g0Pp9rMe/MknK/UspRVa85qZ16WEwGLcebWKyX758Of7xj3/gu+++Q0JCAj777DOsXLkS586dg49P/VFxrWmyNxqN2PfOPkTUytFZTSlKCYVvLXm+mPLICHJymKfMaDTi4KrzKDiVjzATUASK/rMGNKhMG4PBYNwMrHtvOeJq/JGsysVdH9zX1s1hMBg3OTe9D2lCQgJ69eqFr776CgDA8zyCg4Px/PPPY/r06fUe35oCaU5mJnZuXoGKazwMpRSuPIGTqOg0UqCIUPAqwNWfg0x5Z1b4YDAYDAaDceswZPR98A8KavHr3NSJ8fV6PY4ePYoZM2ZI33Ech2HDhuHAgQN2j6mpqUFNjSXxellZWYu308zOzSuQu32n9P98B/vlX2id9jAYDAaDwWDcCDsBPPTUK23dDCta3Ye0oKAAJpMJvr6+Vt/7+voiNzfX7jFz586Fm5ub9AkOtq37zmAwGAwGg8G4NbklHBhnzJiBV16xSPJlZWWtJpQOGX0fdta/G4PBYDAYDMYtwZDRN58PeKsLpF5eXpDJZMjLy7P6Pi8vD35+9qPOVSoVVCrH9d5bEv+goJtOrc1gMBgMBoNxO9HqJnulUomePXtix44d0nc8z2PHjh3o29dxmUoGg8FgMBgMxu1Jm5jsX3nlFTzyyCOIj49H79698dlnn6GyshKPPfZYWzSHwWAwGAwGg9GGtIlAev/99yM/Px/vvfcecnNz0b17d2zZssUm0InBYDAYDAaDcfvDatkzGAwGg8FgMJodVsuewWAwGAwGg3HLwARSBoPBYDAYDEabwgRSBoPBYDAYDEabwgRSBoPBYDAYDEabwgRSBoPBYDAYDEabwgRSBoPBYDAYDEabckvUsr8ec6aqsrKyNm4Jg8FgMBgMBsMeZjmtIRlGb0mBtLy8HAAQHBzcxi1hMBgMBoPBYNRFeXk53Nzc6tznlkyMz/M8srOz4eLiAkJIi1+vrKwMwcHByMjIYIn4Gwnru6bD+q7psL5rOqzvmgbrt6bD+q7p3Ox9RylFeXk5AgICwHF1e4nekhpSjuMQFBTU6td1dXW9KX/wWwHWd02H9V3TYX3XdFjfNQ3Wb02H9V3TuZn7rj7NqBkW1MRgMBgMBoPBaFOYQMpgMBgMBoPBaFOYQNoAVCoVZs6cCZVK1dZNueVgfdd0WN81HdZ3TYf1XdNg/dZ0WN81ndup727JoCYGg8FgMBgMxu0D05AyGAwGg8FgMNoUJpAyGAwGg8FgMNoUJpAyGAwGg8FgMNoUJpAyGAwGg8FgMNqUO04g/frrrxEWFga1Wo2EhAQcOnRI2lZdXY1p06ahXbt20Gq1mDx5MvLy8qyOT09Px9ixY+Hk5AQfHx+8/vrrMBqN0vacnBxMmTIFHTt2BMdxeOmll1rr1lqUG+23w4cPY+jQoXB3d4eHhwdGjhyJEydOWJ3j0UcfRZcuXSCXyzFx4sTWurUWp66+++GHH5CUlARXV1cQQlBSUmL3HJs2bUJCQgI0Gg08PDxs+ueFF15Az549oVKp0L1795a7mVbGUd8VFRXh+eefR1RUFDQaDUJCQvDCCy+gtLTU7nkKCwsRFBRk08e36/sK1P3c/fOf/0R4eDg0Gg28vb0xYcIEnD17Vtp+4sQJPPjggwgODoZGo0FMTAw+//xzq/PfqX1nhlKK0aNHgxCCtWvXWm27U+cJ4Mb77k6dK+rqt6SkJBBCrD7/+te/bM6xYMECdO3aFWq1Gj4+Ppg2bZq07VbotztKIF2+fDleeeUVzJw5E8eOHUO3bt0wcuRIXLt2DQDw8ssvY8OGDVi5ciX27NmD7Oxs3H333dLxJpMJY8eOhV6vx19//YVffvkFCxYswHvvvSftU1NTA29vb7zzzjvo1q1bq99jS3Cj/VZRUYFRo0YhJCQEf//9N/bt2wcXFxeMHDkSBoMBgNC3Go0GL7zwAoYNG9Ym99kS1Nd3VVVVGDVqFN566y2H51i9ejUefvhhPPbYYzhx4gT279+PKVOm2Oz3+OOP4/7772+xe2lt6uq77OxsZGdn49NPP0VKSgoWLFiALVu24IknnrB7rieeeAJdu3a1+f52fF+B+p+7nj17Yv78+Thz5gy2bt0KSilGjBgBk8kEADh69Ch8fHywaNEipKam4u2338aMGTPw1VdfSde4U/vOzGeffWa3dPWdOk8AN953d+pc0ZB+e+qpp5CTkyN9PvnkE6tz/Oc//8Hbb7+N6dOnIzU1Fdu3b8fIkSOl7bdEv9E7iN69e9Np06ZJ/zeZTDQgIIDOnTuXlpSUUIVCQVeuXCltP3PmDAVADxw4QCml9Pfff6ccx9Hc3Fxpn2+//Za6urrSmpoam+slJibSF198seVuqJW40X47fPgwBUDT09OlfU6ePEkB0AsXLthc75FHHqETJkxouRtqRerqu9rs2rWLAqDFxcVW3xsMBhoYGEh/+umnBl1v5syZtFu3bjfa7JuChvadmRUrVlClUkkNBoPV99988w1NTEykO3bssNvHZm6X95XSxvfdiRMnKAB68eJFh+d89tln6eDBg+1uu9P6Ljk5mQYGBtKcnBwKgK5Zs0badqfOE5TeeN/dqXNFff1W3zNSVFRENRoN3b59e4Oud7P22x2jIdXr9Th69KjVyoDjOAwbNgwHDhzA0aNHYTAYrLZHR0cjJCQEBw4cAAAcOHAAXbp0ga+vr7TPyJEjUVZWhtTU1Na7mVakOfotKioK7dq1w//+9z/o9XrodDr873//Q0xMDMLCwlr7llqN+vquIRw7dgxZWVngOA49evSAv78/Ro8ejZSUlJZq9k1BU/qutLQUrq6ukMvl0nenT5/GBx98gF9//RUcd2cMd43tu8rKSsyfPx/t27dHcHCww/OWlpbC09OzRdp8s9CQvquqqsKUKVPw9ddfw8/Pz+Ycd+I8ATRP392Jc0VD39fFixfDy8sLsbGxmDFjBqqqqqRt27ZtA8/zyMrKQkxMDIKCgnDfffchIyOjVe/lRrkzRmgABQUFMJlMVoMEAPj6+iI3Nxe5ublQKpVwd3e3ux0AcnNz7R5v3nY70hz95uLigt27d2PRokXQaDTQarXYsmULNm/ebCU83G7U13cN4dKlSwCAWbNm4Z133sHGjRvh4eGBpKQkFBUVNXubbxYa23cFBQWYPXs2nn76aem7mpoaPPjgg/i///s/hISEtHibbxYa2nfffPMNtFottFotNm/ejG3btkGpVNo9519//YXly5db9e/tSEP67uWXX0a/fv0wYcIEu+e4E+cJoHn67k6cKxrSb1OmTMGiRYuwa9cuzJgxAwsXLsTUqVOlfS9dugSe5/HRRx/hs88+w6pVq1BUVIThw4dDr9e36v3cCHeMQMpoO3Q6HZ544gn0798fBw8exP79+xEbG4uxY8dCp9O1dfNuanieBwC8/fbbmDx5suT7RwjBypUr27h1NwdlZWUYO3YsOnXqhFmzZknfz5gxAzExMVYDN8PCQw89hOTkZOzZswcdO3bEfffdh+rqapv9UlJSMGHCBMycORMjRoxog5bePKxfvx47d+7EZ5991tZNueVoSN+xucI+Tz/9NEaOHIkuXbrgoYcewq+//oo1a9YgLS0NgDBPGAwGfPHFFxg5ciT69OmDpUuX4sKFC9i1a1cbt77h3DECqZeXF2QymU30d15eHvz8/ODn5we9Xm8T5WzeDgB+fn52jzdvux1pjn5bsmQJrly5gvnz56NXr17o06cPlixZgsuXL2PdunWtdSutTn191xD8/f0BAJ06dZK+U6lU6NChA9LT05uvsTcZDe278vJyjBo1Ci4uLlizZg0UCoW0befOnVi5ciXkcjnkcjmGDh0qnXvmzJmtcyNtQEP7zs3NDZGRkRg0aBBWrVqFs2fPYs2aNVbHnD59GkOHDsXTTz+Nd955p1Xa35bU13c7d+5EWloa3N3dpecKACZPnoykpCQAd+Y8ATRP392Jc0VT5omEhAQAwMWLFwHYnye8vb3h5eV1S80Td4xAqlQq0bNnT+zYsUP6jud57NixA3379kXPnj2hUCistp87dw7p6eno27cvAKBv3744deqUVeTbtm3b4OrqavUg3E40R79VVVWB4zirqErz/80awNuR+vquIZhTOZ07d076zmAw4MqVKwgNDW32Nt8sNKTvysrKMGLECCiVSqxfvx5qtdrqHKtXr8aJEydw/PhxHD9+HD/99BMA4M8//7RKh3K70ZTnjlIKSilqamqk71JTUzF48GA88sgjmDNnTou3+2agvr6bPn06Tp48KT1Tx48fBwD897//xfz58wHcmfME0Dx9dyfOFU15X819ZxZE+/fvDwBW80RRUREKCgpurXmiraOqWpNly5ZRlUpFFyxYQE+fPk2ffvpp6u7uLkVD/utf/6IhISF0586d9MiRI7Rv3760b9++0vFGo5HGxsbSESNG0OPHj9MtW7ZQb29vOmPGDKvrJCcn0+TkZNqzZ086ZcoUmpycTFNTU1v1XpuTG+23M2fOUJVKRZ955hl6+vRpmpKSQqdOnUrd3Nxodna2tF9qaipNTk6m48ePp0lJSVI/3srU13c5OTk0OTmZ/vjjjxQA3bt3L01OTqaFhYXSOV588UUaGBhIt27dSs+ePUufeOIJ6uPjQ4uKiqR9Lly4QJOTk+k///lP2rFjR6nv7EX13irU1XelpaU0ISGBdunShV68eJHm5ORIH6PRaPd8jjIZ3G7vK6V1911aWhr96KOP6JEjR+jVq1fp/v376fjx46mnpyfNy8ujlFJ66tQp6u3tTadOnWrVt9euXbO6zp3Wd/bAdZHid+o8QemN992dOlfU1W8XL16kH3zwAT1y5Ai9fPkyXbduHe3QoQMdNGiQ1TkmTJhAO3fuTPfv309PnTpFx40bRzt16kT1er20z83eb3eUQEoppV9++SUNCQmhSqWS9u7dmx48eFDaptPp6LPPPks9PDyok5MTnTRpEs3JybE6/sqVK3T06NFUo9FQLy8v+uqrr9qkmQFg8wkNDW2N22sxbrTf/vjjD9q/f3/q5uZGPTw86JAhQ6S0UGZCQ0Pt9t2tTl19N3PmTLv3PH/+fGkfvV5PX331Verj40NdXFzosGHDaEpKitU1EhMT7Z7n8uXLrXSXLYOjvjMLl425Z0cC6e34vlLquO+ysrLo6NGjqY+PD1UoFDQoKIhOmTKFnj17VjrW0XN5fb/caX1nj+uFKkrv3HmC0hvvuzt1rnDUb+np6XTQoEHU09OTqlQqGhERQV9//XVaWlpqdXxpaSl9/PHHqbu7O/X09KSTJk2ySp9F6c3fb4RSSptB0cpgMBgMBoPBYDSJO8aHlMFgMBgMBoNxc8IEUgaDwWAwGAxGm8IEUgaDwWAwGAxGm8IEUgaDwWAwGAxGm8IEUgaDwWAwGAxGm8IEUgaDwWAwGAxGm8IEUgaDwWAwGAxGm8IEUgaDwWAwGAxGm8IEUgaDwWAwGAxGm8IEUgaDwWAwGAxGm8IEUgaDwbiJoJTCaDS2dTMYDAajVWECKYPBYLQwPM9j7ty5aN++PTQaDbp164ZVq1YBAHbv3g1CCDZv3oyePXtCpVJh3759SEtLw4QJE+Dr6wutVotevXph+/btbXwnDAaD0TLI27oBDAaDcbszd+5cLFq0CN999x0iIyOxd+9eTJ06Fd7e3tI+06dPx6effooOHTrAw8MDGRkZGDNmDObMmQOVSoVff/0V48ePx7lz5xASEtKGd8NgMBjND6GU0rZuBIPBYNyu1NTUwNPTE9u3b0ffvn2l75988klUVVXh6aefxuDBg7F27VpMmDChznPFxsbiX//6F5577rmWbjaDwWC0KkxDymAwGC3IxYsXUVVVheHDh1t9r9fr0aNHD+n/8fHxVtsrKiowa9YsbNq0CTk5OTAajdDpdEhPT2+VdjMYDEZrwgRSBoPBaEEqKioAAJs2bUJgYKDVNpVKhbS0NACAs7Oz1bbXXnsN27Ztw6effoqIiAhoNBrcc8890Ov1rdNwBoPBaEWYQMpgMBgtSKdOnaBSqZCeno7ExESb7WaB9Hr279+PRx99FJMmTQIgCLZXrlxpyaYyGAxGm8EEUgaDwWhBXFxc8Nprr+Hll18Gz/MYMGAASktLsX//fri6uiI0NNTucZGRkfjtt98wfvx4EELw7rvvguf5Vm49g8FgtA5MIGUwGIwWZvbs2fD29sbcuXNx6dIluLu7Iy4uDm+99ZZDIfM///kPHn/8cfTr1w9eXl548803UVZW1sotZzAYjNaBRdkzGAwGg8FgMNoUlhifwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9GmMIGUwWAwGAwGg9Gm/D/1Dzj2jDvxEAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print number of NaNs per era\n",
    "nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n",
    "nans_per_era[target_names].plot(figsize=(8, 4), title=\"Number of NaNs per Era\", legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target correlations\n",
    "\n",
    "The targets have a wide range of correlations with each other even though they are all fundamentally related, which should allow the construction of diverse models that ensemble together nicely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot correlation matrix of targets\n",
    "import seaborn as sns\n",
    "sns.heatmap(targets_df[target_names].corr(), cmap=\"coolwarm\", xticklabels=False, yticklabels=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are ultimately trying to predict the main target (`target_cyrus_v4_20`), it is perhaps most important to consider each auxilliary target's correlation to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>corr_with_cyrus_v4_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>target_cyrus_v4_20</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_xerxes_v4_20</th>\n",
       "      <td>0.951474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_caroline_v4_20</th>\n",
       "      <td>0.938870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_sam_v4_20</th>\n",
       "      <td>0.923488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ralph_v4_20</th>\n",
       "      <td>0.898095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_echo_v4_20</th>\n",
       "      <td>0.853768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_victor_v4_20</th>\n",
       "      <td>0.841411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_nomi_v4_20</th>\n",
       "      <td>0.840038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_william_v4_20</th>\n",
       "      <td>0.837671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_waldo_v4_20</th>\n",
       "      <td>0.833776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_thomas_v4_20</th>\n",
       "      <td>0.808725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_delta_v4_20</th>\n",
       "      <td>0.807416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_bravo_v4_20</th>\n",
       "      <td>0.803201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jeremy_v4_20</th>\n",
       "      <td>0.786834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jerome_v4_20</th>\n",
       "      <td>0.783397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_paul_v4_20</th>\n",
       "      <td>0.765978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_charlie_v4_20</th>\n",
       "      <td>0.765212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ben_v4_20</th>\n",
       "      <td>0.763667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alpha_v4_20</th>\n",
       "      <td>0.761541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_arthur_v4_20</th>\n",
       "      <td>0.704823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_tyler_v4_20</th>\n",
       "      <td>0.694839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_janet_v4_20</th>\n",
       "      <td>0.683992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_george_v4_20</th>\n",
       "      <td>0.682128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alan_v4_20</th>\n",
       "      <td>0.659635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_cyrus_v4_60</th>\n",
       "      <td>0.486983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_xerxes_v4_60</th>\n",
       "      <td>0.484650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_caroline_v4_60</th>\n",
       "      <td>0.482795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_sam_v4_60</th>\n",
       "      <td>0.481005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ralph_v4_60</th>\n",
       "      <td>0.477027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_william_v4_60</th>\n",
       "      <td>0.464474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_echo_v4_60</th>\n",
       "      <td>0.460963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_victor_v4_60</th>\n",
       "      <td>0.459944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_nomi_v4_60</th>\n",
       "      <td>0.459251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_waldo_v4_60</th>\n",
       "      <td>0.455399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_thomas_v4_60</th>\n",
       "      <td>0.448135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_delta_v4_60</th>\n",
       "      <td>0.441717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_bravo_v4_60</th>\n",
       "      <td>0.441140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jeremy_v4_60</th>\n",
       "      <td>0.436126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jerome_v4_60</th>\n",
       "      <td>0.434988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ben_v4_60</th>\n",
       "      <td>0.424300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_charlie_v4_60</th>\n",
       "      <td>0.421626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_paul_v4_60</th>\n",
       "      <td>0.421060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alpha_v4_60</th>\n",
       "      <td>0.420699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_arthur_v4_60</th>\n",
       "      <td>0.391518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_tyler_v4_60</th>\n",
       "      <td>0.390309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_janet_v4_60</th>\n",
       "      <td>0.381520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_george_v4_60</th>\n",
       "      <td>0.376093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alan_v4_60</th>\n",
       "      <td>0.369117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       corr_with_cyrus_v4_20\n",
       "target_cyrus_v4_20                  1.000000\n",
       "target_xerxes_v4_20                 0.951474\n",
       "target_caroline_v4_20               0.938870\n",
       "target_sam_v4_20                    0.923488\n",
       "target_ralph_v4_20                  0.898095\n",
       "target_echo_v4_20                   0.853768\n",
       "target_victor_v4_20                 0.841411\n",
       "target_nomi_v4_20                   0.840038\n",
       "target_william_v4_20                0.837671\n",
       "target_waldo_v4_20                  0.833776\n",
       "target_thomas_v4_20                 0.808725\n",
       "target_delta_v4_20                  0.807416\n",
       "target_bravo_v4_20                  0.803201\n",
       "target_jeremy_v4_20                 0.786834\n",
       "target_jerome_v4_20                 0.783397\n",
       "target_paul_v4_20                   0.765978\n",
       "target_charlie_v4_20                0.765212\n",
       "target_ben_v4_20                    0.763667\n",
       "target_alpha_v4_20                  0.761541\n",
       "target_arthur_v4_20                 0.704823\n",
       "target_tyler_v4_20                  0.694839\n",
       "target_janet_v4_20                  0.683992\n",
       "target_george_v4_20                 0.682128\n",
       "target_alan_v4_20                   0.659635\n",
       "target_cyrus_v4_60                  0.486983\n",
       "target_xerxes_v4_60                 0.484650\n",
       "target_caroline_v4_60               0.482795\n",
       "target_sam_v4_60                    0.481005\n",
       "target_ralph_v4_60                  0.477027\n",
       "target_william_v4_60                0.464474\n",
       "target_echo_v4_60                   0.460963\n",
       "target_victor_v4_60                 0.459944\n",
       "target_nomi_v4_60                   0.459251\n",
       "target_waldo_v4_60                  0.455399\n",
       "target_thomas_v4_60                 0.448135\n",
       "target_delta_v4_60                  0.441717\n",
       "target_bravo_v4_60                  0.441140\n",
       "target_jeremy_v4_60                 0.436126\n",
       "target_jerome_v4_60                 0.434988\n",
       "target_ben_v4_60                    0.424300\n",
       "target_charlie_v4_60                0.421626\n",
       "target_paul_v4_60                   0.421060\n",
       "target_alpha_v4_60                  0.420699\n",
       "target_arthur_v4_60                 0.391518\n",
       "target_tyler_v4_60                  0.390309\n",
       "target_janet_v4_60                  0.381520\n",
       "target_george_v4_60                 0.376093\n",
       "target_alan_v4_60                   0.369117"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets_df[target_names].corrwith(targets_df[\"target_cyrus_v4_20\"]).sort_values(ascending=False).to_frame(\"corr_with_cyrus_v4_20\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Target Selection\n",
    "\n",
    "Our goal is to create an ensemble of models trained on different targets. But which targets should we use?\n",
    "\n",
    "When deciding which model to ensemble, we should consider a few things:\n",
    "- The performance of the predictions of the model trained on the target vs the main target\n",
    "- The correlation between the target and the main target\n",
    "\n",
    "To keep things simple and fast, let's just arbitrarily pick a few 20-day targets to evaluate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arbitrarily pick a few 20-day target candidates\n",
    "target_candidates = [\"target_cyrus_v4_20\", \"target_waldo_v4_20\", \"target_victor_v4_20\", \"target_xerxes_v4_20\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training and generating validation predictions\n",
    "\n",
    "Like usual we will train on the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009872 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2915\n",
      "[LightGBM] [Info] Number of data points in the train set: 606176, number of used features: 583\n",
      "[LightGBM] [Info] Start training from score 0.499979\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010464 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2915\n",
      "[LightGBM] [Info] Number of data points in the train set: 606176, number of used features: 583\n",
      "[LightGBM] [Info] Start training from score 0.500027\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.413319 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2915\n",
      "[LightGBM] [Info] Number of data points in the train set: 606176, number of used features: 583\n",
      "[LightGBM] [Info] Start training from score 0.500008\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010311 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2915\n",
      "[LightGBM] [Info] Number of data points in the train set: 606176, number of used features: 583\n",
      "[LightGBM] [Info] Start training from score 0.499991\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
     ]
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "models = {}\n",
    "for target in target_candidates:\n",
    "    model = lgb.LGBMRegressor(\n",
    "        n_estimators=2000,\n",
    "        learning_rate=0.01,\n",
    "        max_depth=5,\n",
    "        num_leaves=2**5-1,\n",
    "        colsample_bytree=0.1\n",
    "    )\n",
    "    model.fit(\n",
    "        train[feature_cols],\n",
    "        train[target]\n",
    "    );\n",
    "    models[target] = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will generate predictions on the validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-30 17:32:20,886 INFO numerapi.utils: target file already exists\n",
      "2023-08-30 17:32:20,886 INFO numerapi.utils: download complete\n"
     ]
    }
   ],
   "source": [
    "# Download validation data \n",
    "napi.download_dataset(\"v4.2/validation_int8.parquet\");\n",
    "\n",
    "# Load the validation data, filtering for data_type == \"validation\"\n",
    "validation = pd.read_parquet(\"v4.2/validation_int8.parquet\", columns=[\"era\", \"data_type\"] + feature_cols + target_cols) \n",
    "validation = validation[validation[\"data_type\"] == \"validation\"]\n",
    "del validation[\"data_type\"]\n",
    "\n",
    "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n",
    "# Comment out the line below to use all the data\n",
    "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n",
    "\n",
    "# Embargo overlapping eras from training data\n",
    "last_train_era = int(train[\"era\"].unique()[-1])\n",
    "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n",
    "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction_target_cyrus_v4_20</th>\n",
       "      <th>prediction_target_waldo_v4_20</th>\n",
       "      <th>prediction_target_victor_v4_20</th>\n",
       "      <th>prediction_target_xerxes_v4_20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n002a15bc5575bbb</th>\n",
       "      <td>0.507566</td>\n",
       "      <td>0.507919</td>\n",
       "      <td>0.501512</td>\n",
       "      <td>0.506677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00309caaa0f955e</th>\n",
       "      <td>0.509038</td>\n",
       "      <td>0.515242</td>\n",
       "      <td>0.512600</td>\n",
       "      <td>0.505294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00576b397182463</th>\n",
       "      <td>0.495131</td>\n",
       "      <td>0.492790</td>\n",
       "      <td>0.493589</td>\n",
       "      <td>0.493629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00633405d59c6a1</th>\n",
       "      <td>0.500930</td>\n",
       "      <td>0.501813</td>\n",
       "      <td>0.500687</td>\n",
       "      <td>0.498394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n008c2eefc8911c7</th>\n",
       "      <td>0.496661</td>\n",
       "      <td>0.496379</td>\n",
       "      <td>0.496833</td>\n",
       "      <td>0.494594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd5af15959f152</th>\n",
       "      <td>0.489949</td>\n",
       "      <td>0.488280</td>\n",
       "      <td>0.492154</td>\n",
       "      <td>0.488867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd9899640fa670</th>\n",
       "      <td>0.503236</td>\n",
       "      <td>0.503446</td>\n",
       "      <td>0.509806</td>\n",
       "      <td>0.504726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffdc9ed5105d9c3</th>\n",
       "      <td>0.508867</td>\n",
       "      <td>0.512292</td>\n",
       "      <td>0.511857</td>\n",
       "      <td>0.512672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff1b65066d6a16</th>\n",
       "      <td>0.517905</td>\n",
       "      <td>0.516812</td>\n",
       "      <td>0.518501</td>\n",
       "      <td>0.514597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff71d3516eaf1d</th>\n",
       "      <td>0.485958</td>\n",
       "      <td>0.487103</td>\n",
       "      <td>0.491599</td>\n",
       "      <td>0.488686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>632830 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prediction_target_cyrus_v4_20  \\\n",
       "id                                                \n",
       "n002a15bc5575bbb                       0.507566   \n",
       "n00309caaa0f955e                       0.509038   \n",
       "n00576b397182463                       0.495131   \n",
       "n00633405d59c6a1                       0.500930   \n",
       "n008c2eefc8911c7                       0.496661   \n",
       "...                                         ...   \n",
       "nffd5af15959f152                       0.489949   \n",
       "nffd9899640fa670                       0.503236   \n",
       "nffdc9ed5105d9c3                       0.508867   \n",
       "nfff1b65066d6a16                       0.517905   \n",
       "nfff71d3516eaf1d                       0.485958   \n",
       "\n",
       "                  prediction_target_waldo_v4_20  \\\n",
       "id                                                \n",
       "n002a15bc5575bbb                       0.507919   \n",
       "n00309caaa0f955e                       0.515242   \n",
       "n00576b397182463                       0.492790   \n",
       "n00633405d59c6a1                       0.501813   \n",
       "n008c2eefc8911c7                       0.496379   \n",
       "...                                         ...   \n",
       "nffd5af15959f152                       0.488280   \n",
       "nffd9899640fa670                       0.503446   \n",
       "nffdc9ed5105d9c3                       0.512292   \n",
       "nfff1b65066d6a16                       0.516812   \n",
       "nfff71d3516eaf1d                       0.487103   \n",
       "\n",
       "                  prediction_target_victor_v4_20  \\\n",
       "id                                                 \n",
       "n002a15bc5575bbb                        0.501512   \n",
       "n00309caaa0f955e                        0.512600   \n",
       "n00576b397182463                        0.493589   \n",
       "n00633405d59c6a1                        0.500687   \n",
       "n008c2eefc8911c7                        0.496833   \n",
       "...                                          ...   \n",
       "nffd5af15959f152                        0.492154   \n",
       "nffd9899640fa670                        0.509806   \n",
       "nffdc9ed5105d9c3                        0.511857   \n",
       "nfff1b65066d6a16                        0.518501   \n",
       "nfff71d3516eaf1d                        0.491599   \n",
       "\n",
       "                  prediction_target_xerxes_v4_20  \n",
       "id                                                \n",
       "n002a15bc5575bbb                        0.506677  \n",
       "n00309caaa0f955e                        0.505294  \n",
       "n00576b397182463                        0.493629  \n",
       "n00633405d59c6a1                        0.498394  \n",
       "n008c2eefc8911c7                        0.494594  \n",
       "...                                          ...  \n",
       "nffd5af15959f152                        0.488867  \n",
       "nffd9899640fa670                        0.504726  \n",
       "nffdc9ed5105d9c3                        0.512672  \n",
       "nfff1b65066d6a16                        0.514597  \n",
       "nfff71d3516eaf1d                        0.488686  \n",
       "\n",
       "[632830 rows x 4 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate validation predictions for each model\n",
    "for target in target_candidates:\n",
    "    validation[f\"prediction_{target}\"] = models[target].predict(validation[feature_cols])\n",
    "    \n",
    "pred_cols = [f\"prediction_{target}\" for target in target_candidates]\n",
    "validation[pred_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the performance of each model\n",
    "\n",
    "Now we can evaluate the performance of our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "\n",
    "def numerai_corr(preds, target):\n",
    "    ranked_preds = (preds.rank(method=\"average\").values - 0.5) / preds.count()\n",
    "    gauss_ranked_preds = stats.norm.ppf(ranked_preds)\n",
    "    centered_target = target - target.mean()\n",
    "    preds_p15 = np.sign(gauss_ranked_preds) * np.abs(gauss_ranked_preds) ** 1.5\n",
    "    target_p15 = np.sign(centered_target) * np.abs(centered_target) ** 1.5\n",
    "    return np.corrcoef(preds_p15, target_p15)[0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see in the performance chart below, models trained on the auxiliary target are able to predict the main target pretty well, but the model trained on the main target performs the best. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations = {}\n",
    "cumulative_correlations = {}\n",
    "for target in target_candidates:\n",
    "    correlations[f\"prediction_{target}\"] = validation.groupby(\"era\").apply(lambda d: numerai_corr(d[f\"prediction_{target}\"], d[\"target\"]))\n",
    "    cumulative_correlations[f\"prediction_{target}\"] = correlations[f\"prediction_{target}\"].cumsum() \n",
    "\n",
    "cumulative_correlations = pd.DataFrame(cumulative_correlations)\n",
    "cumulative_correlations.plot(title=\"Cumulative Correlation of validation Predictions\", figsize=(10, 6), xticks=[]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the summary metrics below:\n",
    "- the model trained on `waldo` has by far the lowest mean (0.021784), so even though it is not the most correlated with `cyrus` (81%), it might not help actually help our performance \n",
    "- the model trained on `xerxes` has the highest mean (0.023378), but is also very correlated with `cyrus` (94%), so we might not see much effect from ensembling\n",
    "- the model trained on `victor` has almost the same mean as `xerxes` (0.023168), and is much less correlated with `cyrus` (83%), so looks very promising "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>mean_pred_corr_with_cyrus</th>\n",
       "      <th>mean_corr_with_cryus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction_target_cyrus_v4_20</th>\n",
       "      <td>0.023814</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>1.071638</td>\n",
       "      <td>0.056556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_waldo_v4_20</th>\n",
       "      <td>0.021784</td>\n",
       "      <td>0.020933</td>\n",
       "      <td>1.040642</td>\n",
       "      <td>0.054718</td>\n",
       "      <td>0.882217</td>\n",
       "      <td>0.816152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_victor_v4_20</th>\n",
       "      <td>0.023168</td>\n",
       "      <td>0.020852</td>\n",
       "      <td>1.111025</td>\n",
       "      <td>0.050773</td>\n",
       "      <td>0.821906</td>\n",
       "      <td>0.829017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_xerxes_v4_20</th>\n",
       "      <td>0.023378</td>\n",
       "      <td>0.021877</td>\n",
       "      <td>1.068604</td>\n",
       "      <td>0.052989</td>\n",
       "      <td>0.958048</td>\n",
       "      <td>0.940916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   mean      std   sharpe  max_drawdown  \\\n",
       "prediction_target_cyrus_v4_20  0.023814 0.022222 1.071638      0.056556   \n",
       "prediction_target_waldo_v4_20  0.021784 0.020933 1.040642      0.054718   \n",
       "prediction_target_victor_v4_20 0.023168 0.020852 1.111025      0.050773   \n",
       "prediction_target_xerxes_v4_20 0.023378 0.021877 1.068604      0.052989   \n",
       "\n",
       "                                mean_pred_corr_with_cyrus  \\\n",
       "prediction_target_cyrus_v4_20                    1.000000   \n",
       "prediction_target_waldo_v4_20                    0.882217   \n",
       "prediction_target_victor_v4_20                   0.821906   \n",
       "prediction_target_xerxes_v4_20                   0.958048   \n",
       "\n",
       "                                mean_corr_with_cryus  \n",
       "prediction_target_cyrus_v4_20               1.000000  \n",
       "prediction_target_waldo_v4_20               0.816152  \n",
       "prediction_target_victor_v4_20              0.829017  \n",
       "prediction_target_xerxes_v4_20              0.940916  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_metrics = {}\n",
    "for target in target_candidates:\n",
    "    # per era correlation between this target and cyrus \n",
    "    mean_corr_with_cryus = validation.groupby(\"era\").apply(lambda d: d[target].corr(d[\"target_cyrus_v4_20\"])).mean()\n",
    "    # per era correlation between predictions of the model trained on this target and cyrus\n",
    "    mean = correlations[f\"prediction_{target}\"].mean()\n",
    "    std = correlations[f\"prediction_{target}\"].std()\n",
    "    sharpe = mean / std\n",
    "    rolling_max = cumulative_correlations[f\"prediction_{target}\"].expanding(min_periods=1).max()\n",
    "    max_drawdown = (rolling_max - cumulative_correlations[f\"prediction_{target}\"]).max()\n",
    "    summary_metrics[f\"prediction_{target}\"] = {\n",
    "        \"mean\": mean,\n",
    "        \"std\": std,\n",
    "        \"sharpe\": sharpe,\n",
    "        \"max_drawdown\": max_drawdown,\n",
    "        \"mean_corr_with_cryus\": mean_corr_with_cryus,\n",
    "    }\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "summary = pd.DataFrame(summary_metrics).T\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting our favorite target\n",
    "Based on our observations above, it seems like target `victor` is the best candidate target for our ensemble since it has great performance and it is not too correlated with `cyrus`. What do you think?\n",
    "\n",
    "Note that this target selection heuristic is extremely basic. In your own research, you will most likely want to consider all targets instead of just our favorites, and may want to experiment with different ways of selecting your ensemble targets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Ensembling\n",
    "\n",
    "Now that we have selected our favorite target canidate, let's ensemble our predictions and re-evaluate performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the ensemble\n",
    "\n",
    "For simplicity, we will equal weight the predictions from target `victor` and `cyrus`. Note that this is an extremely basic and arbitrary way of selecting ensemble weights. In your research, you may want to experiment with different ways of setting ensemble weights.\n",
    "\n",
    "Tip: remember to always normalize (percentile rank) your predictions before averaging so that they are comparable!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction_target_cyrus_v4_20</th>\n",
       "      <th>prediction_target_victor_v4_20</th>\n",
       "      <th>ensemble</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n002a15bc5575bbb</th>\n",
       "      <td>0.507566</td>\n",
       "      <td>0.501512</td>\n",
       "      <td>0.668901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00309caaa0f955e</th>\n",
       "      <td>0.509038</td>\n",
       "      <td>0.512600</td>\n",
       "      <td>0.853062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00576b397182463</th>\n",
       "      <td>0.495131</td>\n",
       "      <td>0.493589</td>\n",
       "      <td>0.267272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00633405d59c6a1</th>\n",
       "      <td>0.500930</td>\n",
       "      <td>0.500687</td>\n",
       "      <td>0.535059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n008c2eefc8911c7</th>\n",
       "      <td>0.496661</td>\n",
       "      <td>0.496833</td>\n",
       "      <td>0.360384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd5af15959f152</th>\n",
       "      <td>0.489949</td>\n",
       "      <td>0.492154</td>\n",
       "      <td>0.183465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd9899640fa670</th>\n",
       "      <td>0.503236</td>\n",
       "      <td>0.509806</td>\n",
       "      <td>0.727990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffdc9ed5105d9c3</th>\n",
       "      <td>0.508867</td>\n",
       "      <td>0.511857</td>\n",
       "      <td>0.839304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff1b65066d6a16</th>\n",
       "      <td>0.517905</td>\n",
       "      <td>0.518501</td>\n",
       "      <td>0.951933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff71d3516eaf1d</th>\n",
       "      <td>0.485958</td>\n",
       "      <td>0.491599</td>\n",
       "      <td>0.136814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>632830 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prediction_target_cyrus_v4_20  \\\n",
       "id                                                \n",
       "n002a15bc5575bbb                       0.507566   \n",
       "n00309caaa0f955e                       0.509038   \n",
       "n00576b397182463                       0.495131   \n",
       "n00633405d59c6a1                       0.500930   \n",
       "n008c2eefc8911c7                       0.496661   \n",
       "...                                         ...   \n",
       "nffd5af15959f152                       0.489949   \n",
       "nffd9899640fa670                       0.503236   \n",
       "nffdc9ed5105d9c3                       0.508867   \n",
       "nfff1b65066d6a16                       0.517905   \n",
       "nfff71d3516eaf1d                       0.485958   \n",
       "\n",
       "                  prediction_target_victor_v4_20  ensemble  \n",
       "id                                                          \n",
       "n002a15bc5575bbb                        0.501512  0.668901  \n",
       "n00309caaa0f955e                        0.512600  0.853062  \n",
       "n00576b397182463                        0.493589  0.267272  \n",
       "n00633405d59c6a1                        0.500687  0.535059  \n",
       "n008c2eefc8911c7                        0.496833  0.360384  \n",
       "...                                          ...       ...  \n",
       "nffd5af15959f152                        0.492154  0.183465  \n",
       "nffd9899640fa670                        0.509806  0.727990  \n",
       "nffdc9ed5105d9c3                        0.511857  0.839304  \n",
       "nfff1b65066d6a16                        0.518501  0.951933  \n",
       "nfff71d3516eaf1d                        0.491599  0.136814  \n",
       "\n",
       "[632830 rows x 3 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensemble predictions together with a simple average\n",
    "favorite_targets = [\"target_cyrus_v4_20\", \"target_victor_v4_20\"]\n",
    "ensemble_cols = [f\"prediction_{target}\" for target in favorite_targets]\n",
    "validation[\"ensemble\"] = validation.groupby(\"era\")[ensemble_cols].rank(pct=True).mean(axis=1)\n",
    "\n",
    "# Print the ensemble predictions\n",
    "pred_cols = ensemble_cols + [\"ensemble\"]\n",
    "validation[pred_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating performance of the ensemble\n",
    "Looking at the performance chart below, we can see that the peformance of our ensemble is better than that of each individual model. Is this a result you would have expected or does it surprise you?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations = {}\n",
    "cumulative_correlations = {}\n",
    "for col in pred_cols:\n",
    "    correlations[col] = validation.groupby(\"era\").apply(lambda d: numerai_corr(d[col], d[\"target\"]))\n",
    "    cumulative_correlations[col] = correlations[col].cumsum() \n",
    "\n",
    "cumulative_correlations = pd.DataFrame(cumulative_correlations)\n",
    "cumulative_correlations.plot(title=\"Cumulative Correlation of validation Predictions\", figsize=(10, 6), xticks=[]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the summary metrics below, we can see that our ensemble seems to have better `mean`, `sharpe`, and `max_drawdown` than our original model which is great! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction_target_cyrus_v4_20</th>\n",
       "      <td>0.023814</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>1.071638</td>\n",
       "      <td>0.056556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_victor_v4_20</th>\n",
       "      <td>0.023168</td>\n",
       "      <td>0.020852</td>\n",
       "      <td>1.111025</td>\n",
       "      <td>0.050773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble</th>\n",
       "      <td>0.024633</td>\n",
       "      <td>0.021673</td>\n",
       "      <td>1.136564</td>\n",
       "      <td>0.054608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   mean      std   sharpe  max_drawdown\n",
       "prediction_target_cyrus_v4_20  0.023814 0.022222 1.071638      0.056556\n",
       "prediction_target_victor_v4_20 0.023168 0.020852 1.111025      0.050773\n",
       "ensemble                       0.024633 0.021673 1.136564      0.054608"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_metrics = {}\n",
    "for col in pred_cols:\n",
    "    mean = correlations[col].mean()\n",
    "    std = correlations[col].std()\n",
    "    sharpe = mean / std\n",
    "    rolling_max = cumulative_correlations[col].expanding(min_periods=1).max()\n",
    "    max_drawdown = (rolling_max - cumulative_correlations[col]).max()\n",
    "    summary_metrics[col] = {\n",
    "        \"mean\": mean,\n",
    "        \"std\": std,\n",
    "        \"sharpe\": sharpe,\n",
    "        \"max_drawdown\": max_drawdown,\n",
    "    }\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "summary = pd.DataFrame(summary_metrics).T\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the results above, it seems like ensembling models trained on different targets can be a very fruitful avenue of research.\n",
    "\n",
    "However, it is important to note that whether or not to create an ensemble is completely up to you. In fact, there are many great performing models that don't make use of the auxilliary targets at all.\n",
    "\n",
    "If you are interested in learning more about targets, we highly encourage you to read up on these forum posts\n",
    "- https://forum.numer.ai/t/how-to-ensemble-models/4034\n",
    "- https://forum.numer.ai/t/target-jerome-is-dominating-and-thats-weird/6513"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model Upload\n",
    "To wrap up this notebook, let's pickle and upload our ensemble.\n",
    "\n",
    "As usual, we will be wrapping our submission pipeline into a function. Since we already have our favorite targets and trained models in memory, we can simply reference them in our function.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_ensemble(live_features: pd.DataFrame) -> pd.DataFrame:\n",
    "    # generate predictions from each model\n",
    "    predictions = pd.DataFrame(index=live_features.index)\n",
    "    for target in favorite_targets:\n",
    "        predictions[target] = models[target].predict(live_features[feature_cols])\n",
    "    # ensemble predictions\n",
    "    ensemble = predictions.rank(pct=True).mean(axis=1)\n",
    "    # format submission\n",
    "    submission = ensemble.rank(pct=True, method=\"first\")\n",
    "    return submission.to_frame(\"prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-30 17:32:52,748 INFO numerapi.utils: target file already exists\n",
      "2023-08-30 17:32:52,749 INFO numerapi.utils: download complete\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n00011beb76fb85a</th>\n",
       "      <td>0.251835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00030395c4ae306</th>\n",
       "      <td>0.805465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00062925bf2d5a2</th>\n",
       "      <td>0.859910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n000af1f9814addb</th>\n",
       "      <td>0.317088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n001429f265846a1</th>\n",
       "      <td>0.056281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd6ffb0975a454</th>\n",
       "      <td>0.704527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffe1891a18ae870</th>\n",
       "      <td>0.403548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffe21a8f38193a1</th>\n",
       "      <td>0.265294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffe38a699857c42</th>\n",
       "      <td>0.257749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff9be66a5ceaee</th>\n",
       "      <td>0.664356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4904 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prediction\n",
       "id                          \n",
       "n00011beb76fb85a    0.251835\n",
       "n00030395c4ae306    0.805465\n",
       "n00062925bf2d5a2    0.859910\n",
       "n000af1f9814addb    0.317088\n",
       "n001429f265846a1    0.056281\n",
       "...                      ...\n",
       "nffd6ffb0975a454    0.704527\n",
       "nffe1891a18ae870    0.403548\n",
       "nffe21a8f38193a1    0.265294\n",
       "nffe38a699857c42    0.257749\n",
       "nfff9be66a5ceaee    0.664356\n",
       "\n",
       "[4904 rows x 1 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick test\n",
    "napi.download_dataset(\"v4.2/live_int8.parquet\")\n",
    "live_features = pd.read_parquet(f\"v4.2/live_int8.parquet\", columns=feature_cols)\n",
    "predict_ensemble(live_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the cloudpickle library to serialize your function and its dependencies\n",
    "import cloudpickle\n",
    "p = cloudpickle.dumps(predict_ensemble)\n",
    "with open(\"predict_ensemble.pkl\", \"wb\") as f:\n",
    "    f.write(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download file if running in Google Colab\n",
    "try:\n",
    "    from google.colab import files\n",
    "    files.download('predict_ensemble.pkl')\n",
    "except:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! Now head back to [numer.ai](numer.ai) to upload your model!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
